Pan seared scallops with lemon caper pasta is a gourmet recipe for any occasion! Light angel hair pasta is tossed with a citrus white wine sauce and tomatoes. This shop has been compensated by Collective Bias, Inc. and its advertiser. All opinions are mine alone.
The holiday season is here which means more great meals enjoyed with the people you love! Are you looking for a meal that will delight any day of the week, or to share with others for a special occasion? A dish that's always a winner with my family and friends is a comforting plate of Italian pasta, but I like to add a more gourmet twist.
Pan seared scallops with lemon caper sauce will wow any hungry guests at your dinner table. Not to worry, it's straightforward and effortless to recreate, plus I've got a special ingredient that takes the flavors to the next level!
There are two essential ingredients in this pan seared scallops and lemon caper pasta recipe that makes it so irresistible, the pasta and wine!
I wanted to make this dish light, flavorful and bright. Barilla Angel Hair Pasta adds great tasting, high quality and wholesome, tender pasta to the dish. The thin pasta does an excellent job of absorbing and getting coated with the lemon caper sauce to infuse the flavors into each delicious forkful!
Hey, there wine lovers! I know you traditionally think of enjoying a nice glass wine to pair with your meal, but the flavors of wine can add some beautiful notes to your cooking as well. For the lemon caper sauce, I used Clos du Bois Chardonnay as my secret ingredient to make the taste pop!
If you've ever been wine tasting, you've experienced that each wine has its unique character. This Clos du Bois Chardonnay is bright and refreshing, with lots of fruit forward notes of ripe apple, pear, and sweet lemon drop.
The French style of winemaking under the warm California sun also provides toasty oak, vanilla bean, spice, and butter notes. As you can imagine, all of these complex flavors get infused into the sauce and complements the caramelized scallops like a dream.
Whenever I want to create a special dish, scallops are on top of my list. It adds an element of elegance to the recipe, and it's very simple to prepare.
Dry: The trick to achieving a golden brown crust on scallops is making sure both sides are very dry. Use a paper towel to soak up any moisture before salting and adding to the pan. Any excess moisture will cause the scallops to steam instead of sear.
Keep it hot!: A smoking hot pan and scorching oil are going to give a dark caramelized color on the scallops. Don't move the scallops until you are ready to flip. They will brown and release from the pan on its own. I use a stainless steel pan to get a more attractive surface.
Time: Scallops cook very quickly, about 2 to 3 minutes on each side. Keep a close eye so that you get a tender, buttery texture from the scallops, not tough and rubbery.
The caramelized scallops have a nice golden brown crust and a very mild sweet flavor. The delicate seafood and light angel hair pasta are an excellent pair. If you are looking for other ways to enjoy seafood, try my scallops with roasted red pepper sauce recipe!
Now that you've got beautifully caramelized scallops and lemon caper pasta to bring to the table don't forget to pour a glass of chardonnay! Not only do you have the versatility to cook with the wine, but you also get to sit back and enjoy each delicious sip with your meal.
It's hard not to devour this entire plate of pasta! The delicate angel hair pasta tossed in the fresh, savory and tangy lemon caper white wine sauce and herbs is something you can just eat all on its own. The fresh seafood and light pasta are sure to please for any low key or more fancy occasion!
What other pasta and wine pairings have you tried? Do you cook with wine often? I would love to hear your marvelous creations in the comments section.
What are capers and how do you use them in cooking?
Pan seared scallops with lemon caper pasta is a gourmet recipe for any occasion! Light angel hair pasta is tossed in a citrus white wine sauce and tomatoes.
Dry the scallops very well with a piece of paper towel. Season both sides with salt.
Heat a large stainless steel frying pan over medium-high heat. Add 2 tablespoons olive oil and heat until the oil ripples and begins to smoke.
Add scallops to a hot pan without crowding. Cook the scallops without moving them, until the bottoms are a rich golden brown, about 3 minutes.
Add one tablespoon of butter to the pan. Turn the scallops and caramelize the second side, about 1 1/2 to 2 minutes. Turn off heat but do not discard the pan.
Transfer scallops to a clean plate and lightly season with freshly ground black pepper. Lightly cover to keep warm while making the pasta.
Bring a large pot of salted water to a boil. In the meanwhile make the lemon caper sauce.
In the same pan used to cook the scallops, turn heat to medium and add one tablespoon of butter and garlic. Stir and cook garlic for about 1 minute, until fragrant but not browned.
Increase the heat to medium-high and add 1/2 cup white wine. Simmer, stirring as needed until the wine is reduced by half and you can no longer smell the alcohol, about 3 minutes.
Add the chicken broth, lemon juice, zest, and capers, turn heat to high. Cook until sauce is reduced by half (1/2 cup) about 8 to 10 minutes.
Turn off heat and add one tablespoon of butter, and whisk to combine. Taste sauce, season with salt and pepper as needed.
Cook angel hair pasta in boiling water about 4 minutes until al dente, 5 minutes for more tender pasta. Drain pasta and add to the sauce.
Add parsley and toss to combine. Add sliced tomatoes and gently mix. Season pasta with salt and pepper as desired.
Evenly divide the pasta amounts serving dishes. Top with scallops and freshly grated parmesan cheese.
This recipe serves 4 smaller portions or 2 large portions for a hungrier crowd.
I love scallops, but have never been able to cook them properly. Thank you for the great tips. I'll have to try again. This dish looks like a great place to start!
I made this for dinner tonight, and it was fabulous! My wife, the best cook in Sonoma County (one man's opinion!) was sooo imorssed with this meal. When we each took our fair share, there looked to be enough for a light lunch tomorrow. Thirty minutes later, the pan was empty. We ate it all!!
Whoo hoo Paul! What a huge compliment coming from the best cook in Sonoma County 🙂 I'm so elated to hear that you both enjoyed the meal and have some leftovers to enjoy the next day!
D-A-N-G this looks good. Capers are like my kryptonite: I cannot resist them. This looks so buttery (I know you used olive oil but it still looks "buttery" to me, in a good way) and delicious.
Think you CakeSpy! I have definitely grown a fondness for capers, so much flavors in a little package. It does taste buttery without the butter! Nom!
Great meeting you Jessica! I love that you're a food scientist – and can teach us so much about ingredients and the "whys" and "hows" during cooking.
Pleasure meeting you too Jaden! You have been such an inspiration in so many ways 🙂 So happy to hear that you are a fan of food science. I hope I can help other home cooks gain kitchen confidence through science!
I had that prob where the scallops didn't sear well… Wouldn't the salt also bring out moisture? Because I salted the after drying and put in a cast iron skillet.
Hi Henry! Yes salt could definitely bring out more moisture if it sits too long before sauteing. How hot was the pan? I've found that if I don't let it heat up enough it won't get as strong as a sear. You could also try salting right after cooking to see if that helps with the moisture issue. Great questions!
Great conference you Jessica! I love that you're a food researcher. I love scallops, yet have actually never ever had the ability to cook them properly. Thank you for the wonderful tips. I'll need to try again. This recipe appears like a terrific place to start!
AND I HAVE A MEYER LEMON TREE IN MY BACK YARD !!!!! LOVE LEMON RECIPES !!!
Yay Mimi! I love that you got your scallops locally from the farmer's market 🙂 I am so jealous that you have a meyer lemon tree in your backyard!
Hi Jessica, what a fabulous recipe! I want to make it with Zoodles too! I am really enjoying your recipes!
Made this last night–it was great! Thanks!
Yay! So happy to hear that you enjoyed the scallops Sara!
This was really good. I blistered the tomatoes and it made it even better!
This was a keeper for sure! The only thing I did different was add some diced yellow squash and some asparagus tips. I sautéed them in the pan with the tomatoes after the scallops and added them back at the end. Yum! I can't wait until breakfast for the leftovers!
I love the addition of the extra vegetables John, I will have to try that next time!
I'm wondering if the wine can be substituted for a cooking wine. My husband and I currently aren't drinking and I don't want to waste the rest of the bottle.
Hi Amanda- Absolutely cooking wine could be substituted with white wine. What kind so you have?
I ended up using regular wine. I will have to wait until we have guests to drink the rest! What kind would you suggest for the future?
Hi Shelby- Yes, you can substitute the chicken broth with vegetable broth/stock or seafood stock if available.
Does the zest just go in with the juice?
Thank you for sharing this delicious recipe!! I made with orzo pasta and added Sauge leaf for cooking the scallops, it was ????????????
Incredible! Thank you so much for the scallop cooking tips- first time I've ever made them perfectly! I got lots of compliments.
Whoo hoo! I am so proud of you 🙂 Way to be a rockstar cooking pan seared scallops perfectly!
So good! I drizzled a little lemon juice on the finished product for a little extra zing.
It's a good thing my husband doesn't like foods with alcohol in them, because I ate the whole thing! And I'm a tiny person ????
Thank you Stezy! I seriously have the biggest smile after reading your comments 🙂 So happy that you enjoyed the dish!
I was looking for a pasta dish to go with my scallops, and so glad I found this recipe. I made this 2 days in a row because it was so good! I love capers but didn't have them on hand, but even without them, it still tasted delicious. I'd have to make this again next time with capers (and fresh lemon and parsley). I also didn't have parmesan cheese but it was fine without it (less calories too lol). Thanks for the recipe.
Thank you for your feedback Betsy! I'm happy to hear it still turned out well without the capers.
So good!! I got a bag of the frozen mini scallops for $10 at Earth Fare and seared them in batches. I also choose to add the tomatoes with the broth because I prefer them cooked down and like to "pop" the juice out into the sauce. Overall with the mini scallops it's an affordable weeknight pasta dish!
Wow, you hit the dinner jackpot Kim!
Holy. Moly. One of the best dishes I have ever had. I could have eaten this whole thing by myself! This one is a winner!
Thanks for this recipe Jessica! Absolutely delicious. I too licked the plate clean. Never cooked scallops before and was pretty nervous about it. I think we've found a new favourite! Will definitely try more of your recipes. Thanks again.
I can't use wine in my recipe. Should I substitute something else (more chicken broth) or just cook it without.
Hi Pamela- You could add grape juice or juice omit it from the recipe. Thanks!
Thank you for your feedback Vicki! Angel hair pasta would be amazing!
wondering if this can be served cold as a pasta salad. Going to a pot luck and have to include bay scallops and Meyer lemon. Sounds wonderful.


Chilean La Tercera magazine reported a meteor or unindentified luminous object crash in Chile on July 9, 2012. It attracted the interest of the Chilean air force that monitored the town Inquique after the crash.
The director of the National Emergency Office (Onemi) in the region of Tarapaca, Mario Hernandez, told La Tercera that there is no certainty about the origin of "luminous object" that was seen in the skies of Iquique between 23.30 and 23.40 pm on July 9, 2012 and that the agency responsible for issuing this information is the Air Force of Chile, FACH.
'Indeed there was a very powerful light or brightness, accompanied by a trail that no one knows for sure what it is, we as Onemi monitored to see if it caused any other emergency or any involvement in the region, fortunately this did not happen', he said.
FACH said in a statement that the luminous event 'does not correspond to air traffic of aircraft belonging to the 1st Aviation Brigade, resting on the 'Los condors' base of the FACH'. In addition, they indicate that the sighting information was delivered in the communiqué of the Directorate of Civil Aviation, DGAC.
In that document, the DGAC said they will begin an investigation to determine the nature of the 'luminous object'. ' There is no record of any trace that has been displayed in the North of the Directorate General of Civil Aviation radar network, as also, no report of other nature centres and control towers operating in the national territory, with the exception of the information provided by the crew of a commercial aircraft that spotted a brightness in the space '.
Furthermore, they said that 'the Committee study of anomalous aerial phenomena (CEFAA), dependent of the DGAC, began the process of investigation, which is based on specific reports such as: photos, footage and witness statements of the event'.
Finally, they added that any such specific records, will be sent to centers of studies of aerial phenomena in the world 'to complement the analysis and get a reasonable explanation'.
'As it can be seen, a final explanation for the abnormal aerial phenomenon is subjected to a long process of scientific investigation, which can take an undetermined time and does not always have an answer', the release says.
An exceptional bright light phenomenon was observed in many parts of Ostobothnia, western Finland, just after 5pm on Tuesday. According to the Ursa astronomical association, this was a meteorite which had passed through the Earth's upper atmosphere before hitting the ground. According to Esko Lyytinen of Ursa, the object definitely made it to the ground. He speculated it could be as heavy as dozens of kilograms. The object's final explosion occurred probably between the towns of Kalajoki and Ylivieska. Several observations of the object were made by Ursa at locations across Ostrobothnia. The subsonic blast was heard over a wide area. Amateur astronomers are now eager to find the heavenly body which lies somewhere in the area of Ylivieska and Kalajoki. Meteorites are a rare occurrence whereas meteors, objects that fail to reach the Earth's surface, are more common. For example, during the course of the year, the Earth passes through dust clouds which give rise to a phenomenon known as a "meteor shower." During such an event, streaks of light caused by small rocky particles are visible at regular intervals in the night sky.


An adorable pattern design by Cozy Quarters. This is a pattern that combines a 9 patch block with a log cabin block with chimney corners. Imagine all the pieces in this quilt top. It is certainly an interesting arrangement of light and dark 100% cotton fabrics the was sewn carefully by an Amish quilt piecer. The hand quilting is very nice with 7 hand stitches per inch.
The quilt backing is of 100% tan with brown finely printed vines and coordinates with the quilt top. It is a very practical quilt that will be able to be used over the generations. Just what a quilt was meant for!! So cozy! A high quality quilt.


MERSCH A faced 5 batters in the 3rd.
NOISY TLM 1st - DUL C hit by pitch. JEROME A singled to third base; DUL C advanced to second. JEROME A stole second; DUL C stole third. DE RIDDER P struck out swinging. CORNAILLE L walked. MEYER E singled to left field, RBI; CORNAILLE L advanced to second; JEROME A advanced to third; DUL C scored. Outs are set to 3. 1 run, 2 hits, 0 errors, 3 LOB.
PATRIOTS 1st - MERSCH A struck out swinging, out at first c to 1b. LECOMTE N struck out swinging, out at first c to 1b. RODRIGUEZ T singled to second base. RODRIGUEZ T stole second. RODRIGUEZ T advanced to third on a passed ball. DEGOUSEE L struck out swinging, out at first c to 1b. 0 runs, 1 hit, 0 errors, 1 LOB.
NOISY TLM 2nd - JEROME A placed on third. CORNAILLE L placed on second. MEYER E placed on first. FRANCOIS S struck out swinging. SAURAT L walked, RBI; MEYER E advanced to second; CORNAILLE L advanced to third; JEROME A scored. SEASAU A struck out swinging. Outs are set to 3. 1 run, 0 hits, 0 errors, 3 LOB.
PATRIOTS 2nd - SAILLANT A hit by pitch. HUET J hit by pitch; SAILLANT A advanced to second. GIRIN AL grounded out to p; HUET J advanced to second; SAILLANT A advanced to third. HUET J advanced to third on an illegal pitch; SAILLANT A scored on an illegal pitch. MONTREER E struck out swinging. GIRIN C struck out swinging. 1 run, 0 hits, 0 errors, 1 LOB.
NOISY TLM 3rd - RODRIGUEZ T to cf. DEGOUSEE L to c. SAILLANT A to ss. Batter set to DUL C. DUL C walked. JEROME A singled to pitcher; DUL C advanced to second. DE RIDDER P walked; JEROME A advanced to second; DUL C advanced to third. CORNAILLE L singled to center field, RBI; DE RIDDER P advanced to second; JEROME A advanced to third; DUL C scored. MEYER E singled to shortstop, RBI; CORNAILLE L advanced to second; DE RIDDER P advanced to third; JEROME A scored. Outs are set to 3. 2 runs, 3 hits, 0 errors, 3 LOB.
PATRIOTS 3rd - DUL C to c. JEROME A to cf. PRUVOST G to lf for FAUL S. MERSCH A struck out swinging. LECOMTE N struck out swinging. RODRIGUEZ T struck out swinging. 0 runs, 0 hits, 0 errors, 0 LOB.
and RODRIGUEZ Tracy; DEGOUSEE Laura.


Or access Computershare on the web at Computershare.com and log into the Investor Centre.
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Trillium Therapeutics is looking for talented individuals to join our team.
Please send your cover letter and resume or any position enquiries to careers@trilliumtherapeutics.com and include the position of interest in the subject line of your e-mail.
At Trillium, employees work in an enriching environment that promotes team building, mutual respect and fairness. Our commitment to fighting cancer begins in our laboratories and board rooms but extends to our community at large with participation in events such as Rexall's OneWalk to Conquer Cancer to raise funds for Princess Margaret Cancer Centre.


text:
Still a Contender: Some Video Stores Are Thriving in New York City
A look at four stores that know their craft.
By Nikki Lohr • 08/08/14 11:00am
Drew Palermo opened We Deliver Videos in 1999. (All photos by Nikki Lohr)
The Upper East Side's We Deliver Videos.
Howard Salen, the manager of Video Room, who has worked there since 1986.
Howard Salen
All of Video Room's 20,000 DVDs
Video Room was Manhattan's first video store when it opened in 1978.
Will Malitek, the owner of Film Noir, opened his store in Greenpoint ten years ago.
Will Malitek
Film Noir owner Will Malitek calls his store "a touch of the past."
Film Noir Storefront
Hole-in-the-wall store Film Noir has about 11,000 DVD titles.
Video Free Brooklyn owner Aaron Hillis behind the counter.
Aaron Hillis
The 375 square-foot Video Free Brooklyn.
Video Free Brooklyn
Slideshow | List
On the corner of 89th Street and First Avenue is something many would consider a delightful relic: a video rental store called We Deliver Videos. Inside, the walls are completely hidden by a glossy rainbow of DVD boxes. Shelves without movies display candy and microwavable popcorn. A Lord of The Rings poster resides in a sleek black frame.
The video store may seem like a dinosaur–and to be sure they are more rare than in the golden age of the 90s and early 2000s. Between 2010 and 2012, the number of video/disc rental stores in New York state went from 451 to 198 according to the U.S. Census Bureau. That steep two-year decline makes the stores look like an endangered commercial species. This past year has seen the shuttering of NYC staples, from Chelsea's Alan's Alley on July 7 to Kim's Video in the East Village, which is set to close at the end of August. It isn't any better across the nation. Blockbuster, which peaked a decade ago at 9,000 locations worldwide, closed the last of its stores this January.
And yet, there are heroic survivors in NYC. Drew Palermo, who opened We Deliver Videos in 1999, is among them. "Look I'm not going to lie to you, it's definitely slowed down," said Mr. Palermo, thumbing through some of the store's almost 51,000 discs. "But we've been doing this for 15 years and my money's still here. We're signing up new members all the time."
In a video world dominated by Netflix, what has made some stores hang on while the Blockbusters have folded? Location, loyal customers and owning hard-to-find films are a few answers. But talk to any two independent video storeowners in the city and they'll offer a variety of reasons for their success. The common denominator? A deep understanding of their neighborhoods and customers.
That insight is what has allowed Video Room, an Upper East Side mainstay and Manhattan's first video store, to keep going for over 35 years, according to its manager, Howard Salen. He has worked there since 1986, and describes the store's customers as "intelligent Manhattanites" who tend to be older. So they curate to just that: offering a wide array of foreign and classic films, as well as new releases by the Woody Allens and Wes Andersons of the film industry as opposed to the Michael Bays. They also have a secret weapon–a video transfer service. Customers bring in home videos of their families that were filmed on VHS and Video Room converts them to DVDs or other updated digital formats. For Video Room, appealing to a younger clientele is a lost cause. They see video stores as antiques. "Ten years ago they're everywhere," said Mr. Salen. "Now they're from a time capsule."
Will Malitek, who opened his Greenpoint video store Film Noir about ten years ago, would disagree. His business survives off young customers – those of the artsy Bedford Avenue scene. Mr. Malitek is the type of cinephile who thinks Japanese director Yasujirō Ozu is mainstream, and his collection mirrors that mindset. Though he has the occasional big film–a copy of Cowboys & Aliens, for example–he specializes in cult movies and obscure foreign films. He used to have a new releases section but got rid of it once Netflix arrived. But he doesn't see Netflix or on-demand as huge competitors since most of the films he offers simply can't be found elsewhere. "Even if you do find some of them, you're not going to find all the extras and that's what my customers want to see," said Mr. Malitek.
Aaron Hillis, the owner of the 375 square-foot Video Free Brooklyn in Cobble Hill, takes a similar view when it comes to the digital age. As a freelance film journalist who has written for The Village Voice and Time Out New York, he comes across obscure films not available on Netflix or elsewhere. But he's quick to add that the store offers mainstream releases as well. "We're not snobs here," he said.
The Woody Allen section at We Deliver Videos. (Photo by Nikki Lohr)
Even for those films, Mr. Hillis doesn't see online availability as competition. He gets new releases before Netflix and will have them after Netflix recycles them, he explained. More than that, he says that watching films online doesn't fit with the mentality of his customers. "When you check out a movie that you have to bring back, it gives the experience more weight and importance than a click," he said. "And there's nostalgia–people miss being able to talk to that film nerd behind the counter."
It's part of why Mr. Hillis doesn't offer delivery–he wants his customers to have the experience of browsing through tangible titles. For the same reason, Film Noir also does not offer delivery. Video Room and We Deliver Videos love their walk-ins, but for them, delivery is vital. Video Room is able to do business all over Manhattan with a delivery range that spans from the southern tip of Manhattan to 110th Street, east to west.
Mr. Palermo estimates that 50% of We Deliver Video's business is made up of online and phone orders. For him, the digital age has had benefits. The store's website has increased business, and even though We Deliver Videos thrives on new releases, Netflix and its brethren haven't had a detrimental effect. Mr. Palermo thinks it has to do with the neighborhood's desire to support a mom-and-pop business. Many of his customers want the face-to-face interaction and suggestions that an online algorithm can't provide. Amusingly enough, one of the store's best-renting TV series is House of Cards. "We can't keep it on the shelves," Mr. Palermo grinned. "I chuckle every time someone wants it and it's not in stock. But I don't say anything."
All the stores say they don't know how they would survive in another neighborhood, let alone outside of New York City. The city's film culture is key. When Mr. Hillis bought Video Free Brooklyn in 2012, a customer in the store congratulated him–she turned out to be the producer of Moonrise Kingdom. Zoe Kazan and Paul Dano are also frequent customers of VFB. Video Room says they have a laundry list of customers who are big shots in the movie business, though they didn't give out names.
Independent ownership has also been key to all of them. It lets them evolve to suit the needs of their neighborhoods. Mr. Hillis has increased his number of titles from 5,000 to 8,500 in the past two years. Mr. Malitek is doing so well that he is planning to open a movie theater in Brooklyn within the next few years, which, like his store, will screen only offbeat films. Even Alan's Alley has been reincarnated, now as a fifth-floor walk-up shop in a building on 25th Street and Seventh Avenue.
Still, times are different. Video Room used to be almost three times the size of its current 79th Street location. "It would be nice in a perfect world to have two thirds of this block," said Mr. Salen "but those days are over."
Filed Under: Entertainment, Politics, News & Politics, slideshow, Greenpoint, Cobble Hill, Upper East Side, video stores

redpajama_set_name: RedPajamaCommonCrawl


In her latest EP, she discusses the tension in feeling unwelcome in the place she calls home.
"Covered up or not, don't ever take us for granted."
The inability to see the face of God in another or even in your enemy is what keeps us divided and is a major contributing factor for the existence of extremist groups.
"We wanted to replace the trauma and terror with love by way of doughnuts, coffee, flowers and good conversations."


text:
Poecilochroa capensis är en spindelart som beskrevs av Embrik Strand 1909. Poecilochroa capensis ingår i släktet Poecilochroa och familjen plattbuksspindlar. Inga underarter finns listade i Catalogue of Life.

Källor

<references>

<ref name = "col275923">Strand, E. (1909g) Spinnentiere von Südafrika und einigen Inseln gesammelt bei der deutschen Südpolar-Expedition., In Deutsche Südpolar-Expedition 1901-1905</b>. Berlin, 10(5): 541-596.</ref>

</references>

Plattbuksspindlar
capensis

redpajama_set_name: RedPajamaWikipedia


text:
Jerome Allen Seinfeld is an American stand-up comedian, actor, writer, and producer. He is best known for playing a semi-fictionalized version of himself in the sitcom Seinfeld, which he created and wrote with Larry David. The show aired on NBC from 1989 until 1998, becoming one of the most acclaimed and popular sitcoms of all time. As a stand-up comedian, Seinfeld specializes in observational comedy. In 2004, Comedy Central named him the 12th-greatest stand-up comedian of all time. Seinfeld produced, co-wrote, and starred in the 2007 film Bee Movie, which was nominated for a Golden Globe Award for Best Animated Feature Film. In 2010, he premiered a reality series called The Marriage Ref, which aired for two seasons on NBC. Seinfeld is the creator and host of the web series Comedians in Cars Getting Coffee. He is married to author and philanthropist Jessica Seinfeld, with whom he has three children.
Jerry Seinfeld: Wife shares family album from getaway with Amy Schumer and even partied with Diddy
Jerry Seinfeld and Wife Jessica Enjoy Romantic Caribbean Vacation: See Swimsuit Photos
Jerry Seinfeld, 68, in wet shirt and wife Jessica, 51, in swimsuit in St Barts
Jerry Seinfeld discusses rise of anti-Semitism and lauds his wife Jessica for post denouncing abuse
Jerry Seinfeld Shares His Thoughts on the Rise of Antisemitism and His Wife's 'Fantastic' Way of Fighting Against It
Jerry Seinfeld cruelly jokes Jay Leno 'wasn't that much to look at before'
Jerry Seinfeld Gives an Update on Jay Leno (Exclusive)
Jerry Seinfeld and wife Jessica land helicopter in New York
Jerry Seinfeld, 68, looks hip in fashion ensembles as he makes his modeling debut for Kith
Lisa Kudrow Explained Why Jerry Seinfeld Can Partially Take Credit For The Success Of "Friends"
Lisa Kudrow reveals Jerry Seinfeld took credit for Friends doing so well
Lisa Kudrow Says Jerry Seinfeld Once Told Her 'You're Welcome' For 'Friends' Success
Lisa Kudrow Recalls Jerry Seinfeld Taking Credit for 'Friends' Success
Lisa Kudrow Recalls Jerry Seinfeld Taking Credit for 'Friends' Success
Jerry Seinfeld is overtaken by a laughing fit as he enjoys coffee with a friend in Malibu
Gwyneth Paltrow puts her arm around Jerry Seinfeld's wife Jessica
Hugh Grant dons a Tony the Tiger suit with Jerry Seinfeld on the set of Pop-Tarts film in LA
Melissa McCarthy, Amy Schumer and Jim Gaffigan join Jerry Seinfeld's Pop-Tarts origin film Unfrosted
Jerry Seinfeld's wife Jessica and daughter Sascha enjoy quality time at Broadway play
Jerry Seinfeld Reveals A-List Cast for Directorial Debut 'Unfrosted: The Pop-Tart Story' - See Which Stars Are in His Movie


text:
Q: Using Java System.in as a press to continue first sorry if the title isn't clear enough but English isn't my main language so it's a bit hard to explain myself.
I made a simple program that requires you to press Enter so it continues with the code, in my case count to 60 second then wait for another Enter, the problem is that sometimes it bugs and continues as if you had pressed Enter when you didn't (might be because accidentally there is another character pressed before Enter key).
Here is the fragment of code, I used System.in.read() because I read here that it was a good option for what I was looking to do, but I was more experienced with Scanner.
public static void cuentaSeries(int n) throws Exception {
   System.out.println("------------------------------");
   int aux = 1;
   while(aux<=n)   {
       System.out.print("Serie Nº" +aux +"  []");
       System.in.read();
       System.out.println("Serie Nº" +aux +"  [X]");
       cuenta();
       aux++;
    }
   System.out.println("------------------------------");
}

public static void cuenta() throws Exception    {
   int contador = 0;
   System.out.print("0 ");
   while(contador<60)   {
       Thread.sleep(1000);
       contador++;
       if(contador%5==0) System.out.print(contador +" ");
       if(contador==60) System.out.println("");
    }
}

Thanks.

A: The problem comes from the line:
System.in.read();

See the documentation that says:

Reads the next byte of data from the input stream. The value byte is
  returned as an int in the range 0 to 255.

You are not checking for the enter key but for any single character.  Consider if you got the input 'hello'.  The System.in.read() will first read 'h', then 'l', etc.  To solve this you need to switch to using nextLine().
Try this:
   Scanner scan = new Scanner(System.in);
   int aux = 1;
   while(aux<=n)   {
       System.out.print("Serie Nº" +aux +"  []");
       scan.nextLine();
       System.out.println("Serie Nº" +aux +"  [X]");
       cuenta();
       aux++;
    }

with
import java.util.Scanner

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

{R}{ecent} advancements in Unmanned Aerial Vehicle (UAV) technologies have accelerated the deployment of UAVs for various applications like visual inspection \cite{3c854a3fe5b44f23bf0b8345d8b67db8}, reconnaissance missions \cite{5600072}, agriculture \cite{fe2adf4814da4b9b8c88ab56963bd240} etc. The current increase in humans' reliance on UAVs is due to their mechanical simplicity and ease of control. Therefore, there is a need to develop robust automation algorithms to enhance the capabilities of a UAV to operate in cluttered environments.

In the literature, the problem of obstacle avoidance has been extensively studied. Studies like \cite{1272530} have been focused on coordinated flights to prevent UAVs from crashing into each other. Similarly, \cite{Han2009ProportionalNC} uses proportional navigation for the same. Many works in UAV navigation like \cite{MANNAR2018480} use sensor data streamed to the ground station for processing, thus leading to huge latency issues. Also, in these works, a known map of the environment is used. In an uncertain environment, it will not yield successful results due to changes in the map. Therefore a sensor-based algorithm enables UAVs to act dynamically based on their observed environment.

In the literature for sensor-based obstacle avoidance, multi-sensor fusion techniques have been used to enhance the UAV's capabilities. For example, in \cite{Fasano2008MultiSensorBasedFA} one radar, four cameras, and two onboard computers have been used. However, it is not a viable option for low-cost fast moving drone operating in a cluttered environment. To overcome this problem, many algorithms using complex local \cite{Daftry2016RobustMF, 7487284, 7989677, Florence2016IntegratedPA} and global \cite{Oleynikova2016ContinuoustimeTO} planners have been designed for obstacle avoidance. However, these algorithms have not shown promising results because their planning modules were constrained due to the sensor's field of vision. Recently a state-of-the-art approach for UAV control with aggressive maneuvers has been presented by \cite{Tordesillas2019RealTimePW}, which could be used for real-time applications. However, these methods require accurate instantaneous state feedback, which can only be delivered by motion capture systems and perform well in indoor environments only. Thus, there is a need to develop a cheap multi-sensor approach to efficiently use the available sensor data for instantaneous decision-making and control in outdoor environments.

Recently in literature, the fusion of laser scanners and RGB-D cameras has been used for obtaining accurate estimates of the environment. In \cite{Bachrach2012EstimationPA},  RGB-D sensors is used to obtain point-cloud data of the environment but these sensors are restricted to very low range in outdoor conditions. Accurate estimation of the obstacles can be obtained either by using stereo cameras \cite{8949363} or monocular cameras by creating depth maps using various Simultaneous Localization and Mapping (SLAM) algorithms \cite{Bachrach2009AutonomousFI}. However, these works are computationally intensive for on-board computers and yield in lower control frequency in position and velocity feedback.

In the absence of data of the entire environment, learning-based motion planners have been commonly used. Approaches related to learning motion policies and depth from input data were demonstrated by \cite{Bills2011AutonomousMF}. It used orthogonal structures of indoor scenes to estimate the vanishing point to navigate the MAV in the corridors by maneuvering towards dominant vanishing points.  Other works  \cite{Schaal1999IsIL, 6287440, Argall2009ASO} have applied various learning techniques like imitation learning to navigate a UAV. In \cite{6630809}, autonomous flight is achieved at a stretch of $3$ km, at a speed of $1.5$ m/s in a cluttered forest environment using the imitation learning method. However, in most cases, due to high computational complexity, the UAV streams data to an external computer and then receives higher-level control commands.
Hence, there is still a need to develop a cost-effective, less computationally intensive obstacle avoidance algorithm for high-speed UAVs in a cluttered environment.

In this paper, we propose a novel Fast Obstacle Avoidance Motion (FOAM) algorithm for sense-and-avoid operations in a cluttered environment. A low-cost monocular camera is used to capture images, which are then processed to obtain the probabilistic occupancy map (POM). A 2D Lidar is also used to generate POM in horiziontal plane and is fused with that of camera images. The resultant POM is used to determine the free space by formulating it as an optimization problem. Here, local planner uses this directional information to generate high-level commands like the desired yaw and linear velocity. It enables the quadcopter to traverse the least occupied path to the desired goal. The performance of the proposed algorithm is evaluated in both simulation and actual environments. First, Gazebo simulations were carried out to verify the performance of the algorithm in a cluttered bugtrap environment. Later, simulations in the AIRSIM simulator were conducted in a forest environment to realize the near outdoor-like conditions. Finally, the proposed algorithm was tested on a custom-built quadcopter that uses Pixhawk autopilot in an outdoor environment. The results clearly indicate that FOAM is computationally less intensive and can handle obstacle at a speed of $4.5$m/s efficiently.

The remaining paper's organisation is as follows: Section II presents the  the formulation of the problem, followed by the description of the proposed algorithm. Section III describes the simulations carried out along with the experimental setup followed by the conclusion.

\section{Fast Obstacle Avoidance Motion (FOAM) Algorithm}

In this section, the problem definition of sense-and-avoid using a high speed quadcopter is provided. The following subsection describes the generation of probability occupancy map (POM) based on monocular camera, followed by POM based on 2D lidar. Later, the fusion of the occupancy map is described along with the information about flight control. Finally, the FOAM algorithm is described followed by that a formal description of the same is provided.

\subsection{Problem Definition}

In this section the sense and avoid (SAA) problem is formulated for a quadcopter $Q$ with $m$ obstacles ($O_1, O_2, \cdots, O_m$) within a region $\bf{A} \subset \Re^2$. A typical SAA problem with a quadcopter and obstacles is shown in Fig. \ref{fig:formuation}. The initial launch position $({X}_o \in \Re^3)$ and the goal position $({X}_g,  \in \Re^3)$ are user defined and can be anywhere within $\bf{A}$. The objective of $Q$ is to move from the initial launch position ${x}_o$ to the goal position ${x}_g$ at a high speed $v$ (i.e, $\geq 4$ m/s). Also due to the presence of obstacles in the path it has to avoid them to achieve a collision free maneuver. The mission is assumed to be a success if $Q$ manages to reach the goal position without any collision. However the mission is terminated if the quadcopter fails to reach the goal position within $t^{max}$, or in case there is a collision. Before the start of the mission, the quadcopter is initialized at $x_o$ with velocity $v = 0$ along with a random heading angle $\psi$.Also the quadcopter is made to maintain a fixed height of $h$ along the z axis all the time. During the mission the kinematics equations for the control of the quadcopter can be given as: $\dot{x} = v*cos(\psi); \dot{y} = v*sin(\psi)$;
where, $\dot{x}$ and $\dot{y}$ represents the velocity of the $Q$ along Cartesian $x$ and $y$ axes.

The quadcopter is equipped with a monocular camera and a 2-D lidar. The 2D lidar provides point-cloud data at a radius of $r$. Every point in the point cloud is associated with two values, i.e., the range measurements ($z_i \leq r$) and the angle from the evader frame of reference. The monocular camera provides visual sensor information within a $90 \degree$ field of view. The orientation of the camera is kept similar to that of the quadcopter. In addition to LIDAR and monocular vision, the quadcopter also has a flight controller that provides inertial measurements and position information of the vehicle. Based on the sensing information, the quadcopter is expected to identify the boundary of obstacles in the sensing region and follow an obstacle free paths.
For example in Fig. \ref{fig:formuation}, the vehicle senses the obstacle ($o_1$) partially and generate a feasible path to reach the goal point as shown in yellow coloured path.

In order to ensure that the quadcopter reaches the goal position optimally, some  additional constraint on its motion has been added. To ensure optimally the quadcopter requires to minimize the deviation from the ideal straight line path joining the initial position and the goal while performing the mission. This has to be implemented using a local planner by minimizing the distance between the quadcopter's position $(x,y)$ from the line joining $(x_o,y_o)$ and $(x_g, y_g)$. Hence, the final mission of the quadcopter is to reach the target location by avoiding collision while having minimum deviation from the optimal path.

\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/problemdef.png}
    \caption{A typical unknown clutter environment highlighting the obstacles, launch position, goal position and sensor information. The green circle around the UAV defines the space UAV occupies in the given map. The blue region is the effective sensing zone of the UAV. The regions $O_1, O_2,…… O_i, …. O_j$ are the obstacles. The dotted line denotes the shortest path from $X_0$ (start point) to $X_g$ (goal point). The yellow path depicts a typical the UAV would follow to avoid the obstacles to reach the goal.}
    \label{fig:formuation}
\end{figure}

\begin{figure}[h]
	\centering
    \subcaptionbox{Camera FOV divided into $s_M$ sectors\label{fig:camera_POM}}
    {\includegraphics[width=0.31\textwidth]{graphics/cam_fov.PNG}}
    \subcaptionbox{Filtered 2D lidar FOV\label{fig:lidar_pom}}
    {\includegraphics[width=0.31\textwidth]{graphics/lidar_fov.PNG}}
    \caption{Occupancy sector-based field-of-view of sensors}
    \label{fig:pom_gen}
\end{figure}

\subsection{Probabilistic Occupancy Map for FOAM}\label{pom}

The problem of detecting obstacles from the current frame $f$ at time $t$ is converted to a free space determination problem. In this case, we use a monocular camera and a 2D lidar as shown in Fig. \ref{fig:pom_gen}. Based on the field of view (FOV) of the monocular camera, the width of the image is divided into $M$ sectors, ${s_1, s_2, ..., s_M}$ (where $M$ is always an odd number) as shown in Fig. \ref{fig:camera_POM}. Similarly, the point cloud data obtained from the 2D lidar is used. Even though a 2D lidar can provide a $360^{\circ}$ field of view, the algorithm selects the region of interest which camera FOV. Hence, the point cloud data is also mapped into $M$ sectors as that of the camera, shown in Fig. \ref{fig:lidar_pom}. The objective to do so is to find the sector free from obstacles which has the minimum deviation from the median sector.

In order to determine the free space available for the quadcopter to navigate towards the goal, we determine the probability of a sector occupied with an obstacle. A monocular camera is used to capture RGB images (30 fps). Each RGB image (640 X 480 resolution) is converted to a gray-scale image. From the gray-scaled image, robust corners are extracted using the Shi-Tomasi corner detector algorithm \cite{323794}. It is shown in the literature that these features are robust to track in an image where the changes are not abrupt. One should note that in a nominal wind condition, the objects present in the cluttered environment do not move abruptly. Hence, the corner features ($x_i$, $i=1,2,\cdots,N$) detected will be useful to identify the free space present in the current frame. Further, the optical flow values of these sparse features set are computed using the Lucas-Kanade method of iterative pyramids \cite{inproceedings}. For this purpose, we use the past three frames to track these corner features. Let $n_i$ be the number of features present in $i$th sector, $v_i$ is the flow for given feature $x_i$ and $N=\sum^m_{i=1}n_i$ be the total number of features with reliable optical flow values in the image frame. Using the flow values of these features, we compute the Probability of Occupancy Map (POM) for $i$th sector as given below,

\begin{equation}\label{Camera_POM}
P_i^c\left(I_t\right)=\frac{\sum_{j=1\ \& \ x_i\ \in s_i}^{n_i}v_j}{\epsilon+\sum_{j=1}^{N}v_j},\ i=1,2,\cdots,m
\end{equation}
where, $s_i$ is the $i^{th}$ sector and the value of $\epsilon$ is close to zero. Note that the flow values for the features detected very far away from the camera will be low, and flow values for features near the camera will be high. Hence, the occupancy value of the sector will be small if the obstacle is far away from the quadcopter and vice-versa. The computational requirement of corner features and optical flow is small, and one achieves 30 frames per second processing speed to detect the free space.

Similarly, a long range 2D lidar is used for depth estimation of the obstacles planar to the quadcopter.
In order to estimate the occupancy in the camera plane, the field of view (FOV) of lidar is restricted to the camera's  FOV, as shown in Fig. \ref{fig:lidar_pom}. The effective depth of the 2D lidar is restricted to $d_{max}$. Based on the resolution of LIDAR, one will always have a finite number of points in each sector.  Note that the depth information of points beyond the desired sensing radius is considered effective depth. It is set at 10m in the simulation. Thus, the probability occupancy map using 2D point cloud data for the $i$th sector is given as

\begin{equation}\label{lidar_POM}
P_i^L\left(I_t\right)=\frac{\sum_{j=1\ \&\ p_i\ \in s_i}^{n_i}{d_{max}-d}_j}{\epsilon+\sum_{j=1}^{N}{d_{max}-d_j}},\ i=1,2,\cdots,m
\end{equation}

From the equation, we can conclude that the probability will be zero if the sector does not have any obstacles.
The probability occupancy map obtained from camera and LIDAR point cloud data are fused as:

\begin{equation}\label{optimisation}
P_i\left(I_t\right)=w_CP_i^C\left(I_t\right)+w_LP_i^L\left(I_t\right),\ i=1,2,\cdots,m
\end{equation}

where $w_C$ and $w_L$ are the weights for camera and LIDAR POM respectively and $w_C + w_L = 1$.

\begin{algorithm}

\caption{Pseudocode FOAM}
\begin{algorithmic}

\Statex
 \State Data: Current image frame $f_k'$, Lidar Data $L$, GPS Data $G$;
 \State $\epsilon$ $\leftarrow$ positive value close to zero;
 \State $H$ $\leftarrow$ empty list;
 \State $P$ $\leftarrow$ empty list;
 \State Algorithm Parameters: $W_C, W_L | W_C + W_L = 2, D_{max}, M$

 \Function{FOAM}{}
    \For{i in $1, 2, ……. M$}
        \State $P_i^C$ $\leftarrow$ POM $image ()$
        \State $P_i^L$ $\leftarrow$ POM $lidar ()$
        \State $P_i$ $\leftarrow$ $W_C P_i^C + W_L P_i^L$
        \State $P$ $\leftarrow$ append ($P_i$)
    \EndFor

    \State $s_d$ $\leftarrow$ minYawCost (P)
    \State currYaw $\leftarrow$ from GPS Data G
\If {$s_d$ $>$ ($\frac{M + 1}{2}$)}
\State yaw $\leftarrow$ currYaw + 10 * (i – ($\frac{M + 1}{2}$))

\ElsIf {$s_d$ $<$ ($\frac{M + 1}{2}$)}
\State yaw $\leftarrow$ currYaw – 10 * (($\frac{M + 1}{2}$) - i)

\Else
\State yaw $\leftarrow$ currYaw
\EndIf

\EndFunction

\Function{POM image} {}

\State Get $f_k'$ from camera
\State $f_k$ $\leftarrow$ convert $f_k'$ to gray-scale image
\State $X_i$ $\leftarrow$ corner features for sector $s_i$ in $f_k$ \cite{323794}
\State $V_i$ $\leftarrow$ optical flow values for given $X_i$ in frames $f_k– 1$,$f_k – 2$,$f_k-3$ \cite{inproceedings}
\State calculate $P_i^C$ using Equation (\ref{Camera_POM})
\State return $P_i^C$

\EndFunction

\Function {POM lidar}{}

\State Get $L$ from lidar
\State $D_j$ $\leftarrow$ lidar data in sector $S_i$ in frame $f_k, D_j \in L$
\State calculate $P_i^l$ using Equation (\ref{lidar_POM})
\State return $P_i^L$
\EndFunction

\Function {minYawCost (P)}{}

\State $P_{min}$ = min(P)
\For {l in 0, 1, ……. ($M$ - 1)}
\If {P = $P_{min}$}
\State H $\leftarrow$ append ($l$ + 1)
\Else
\State H $\leftarrow$ min (abs (h – ($\frac{M + 1}{2}$))) $\forall h \in H$
\EndIf
\EndFor
\EndFunction

\end{algorithmic}
\label{Algo}
\end{algorithm}

\subsection{Flight Control}

\begin{figure}[h]
	\centering
    \includegraphics[width=1\linewidth]{graphics/control.png}
    \caption{A Schematic Diagram of Flight Control}
    \label{fig:control}
\end{figure}

The algorithm is designed to send high-level commands to the quadrotor – (1) forward translational speed, (2) constant height, and (3) rotational velocity (heading/desired yaw). The forward velocity of the complete system is constant throughout the whole mission and can be modified by the user based on the application of the vehicle. Fig. \ref{fig:control} shows the schematic diagram of the flight control used for this algorithm.
As mentioned, the required high-level commands like the desired yaw, height of the vehicle, etc., are sent to the velocity controller present in the Pixhawk autopilot. These velocity commands are sent via mavlink protocol while using the MAVROS package to the autopilot. The Pixhawk is connected to the on-board computer via a serial connection.
The outer loop uses a PID controller for velocity control. It computes the desired thrust, roll, pitch and yaw and sends it to the attitude controller. The PID control of the attitude controller then realises the required motor speed on the quadrotor for the optimal motion. The above-mentioned process is completely realised inside the Pixhawk autopilot of the quadcopter.

\subsection{FOAM Algorithm}

\begin{figure}
	\centering
    \includegraphics[clip,trim=0mm 0mm 0mm 0mm,width=1\linewidth]{graphics/schematic.png}
    \caption{A Schematic Diagram of FOAM System Architecture}
    \label{fig:architecture}
\end{figure}

The FOAM algorithm is designed such that it is computationally less intensive. The system architecture of the same is shown in Fig. \ref{fig:architecture}. The figure illustrates various components of FOAM and its integration into the small quadcopter. First, FOAM generates POM based on the data from the monocular camera for the $xz$-plane. Then it generates POM using the point cloud data from 2D-LIDAR for the $xy$-plane. Next, free space is determined by formulating an optimization problem by fusing the POM's. The solution is to determine the free space at time $t$. Finally, the local planner generates the necessary commands to the Pixhawk flight controller such that the quadcopter avoids the obstacle and move towards the goal position. The above-mentioned algorithm is summarized and given in algorithm \ref{Algo}.

\begin{figure}[h]
	\centering
    \includegraphics[width=1\linewidth]{graphics/gazebo_com.png}
    \caption{Bugtrap (left) and Town (right) environments used in Gazebo simulator.}
    \label{fig:gazebo}
\end{figure}

\begin{figure*}[h]
\centering

    \subcaptionbox{Simulation frame at time 26 \textit{seconds}\label{fig:a2}}
    {\includegraphics[clip,trim=0.5mm 0.5mm 0mm 0mm,width=0.33\textwidth, height=4cm]{graphics/forest1.png}}
    \subcaptionbox{Simulation frame at time 38 \textit{seconds}\label{fig:b2}}
    {\includegraphics[clip,trim=0.5mm 0.5mm 0mm 0mm,width=0.33\textwidth , height=4 cm]{graphics/forest2.png}}
    \subcaptionbox{Simulation frame at time 52 \textit{seconds}\label{fig:c2}}
    {\includegraphics[clip,trim=0.5mm 0.5mm 0mm 0mm,width=0.32\textwidth , height=4 cm]{graphics/forest3.png}}

        \subcaptionbox{Experiment frame at time 66 \textit{seconds}\label{fig:d2}}
    {\includegraphics[clip,trim=1.2mm 1mm 0mm 0mm,width=0.33\textwidth, height=4cm]{graphics/forest4.png}}
    \subcaptionbox{Experiment frame at time 80 \textit{seconds}\label{fig:e2}}
    {\includegraphics[clip,trim=1.2mm 1mm 0mm 0mm,width=0.33\textwidth , height=4 cm]{graphics/forest5.png}}
    \subcaptionbox{Experiment frame at time 98 \textit{seconds}\label{fig:f2}}
    {\includegraphics[clip,trim=1.2mm 1mm 0mm 0mm,width=0.32\textwidth , height=4 cm]{graphics/forest6.png}}

    \caption{Different instances of a AirSim Forest Simulation. The quadcopter flies towards Goal point while avoiding the obstacles.}
    \label{fig:sim_exp_trajectory2}
\end{figure*}

\section{Performance Evaluation}
In order to evaluate the performance of FOAM, different test scenarios are considered. Custom environments were created and tested using the Gazebo simulator using PX4 Flight Stack in the back-end for the 3DR IRIS quadcopter. Simulations were also conducted on AirSim to verify the performance of the algorithm. Finally, to evaluate the entire proposed system, experiments were conducted in an outdoor environment using a custom-built quadrotor vehicle that would autonomously navigate based on the high-level commands sent from the onboard computer. The videos related to the evaluation can be found in the following link: \textbf{https://bit.ly/3sFtVyP}.

\subsection{PX4 ROS Gazebo Simulations}
The proposed algorithm was tested in various environments. A custom python code was designed to compute and provide the required control commands to the 3DR IRIS quadrotor. The 3DR IRIS model has a monocular camera and 2-D lidar plugins integrated into it. The monocular camera uses a horizontal field of view of $90 \degree$ and a sensing range of $10$ m for the 2-D lidar. The sample Gazebo environment is shown in Fig. \ref{fig:gazebo}.

\subsection{AIRSIM Simulation}
Next, the algorithm was tested on AirSim, an open-source simulator to realize the near outdoor-like conditions. The quadcopter model uses a built-in monocular camera along with the 2D LIDAR for perception. Different instances in Fig. \ref{fig:sim_exp_trajectory2} show a typical forest environment in an AirSim simulator. The algorithm was also tested on a custom-designed cluttered environment in AirSim to test its robustness. The simulation was carried out using Intel i7 8750H 3.0 GHz processor, powered with 16GB of DDR4 memory, which also housed an NVIDIA RTX 2070 Max-Q graphics memory.

\begin{figure}[h]
	\centering
    \includegraphics[width=1\linewidth]{graphics/uav1.png}
    \caption{Custom UAV used in the experiments. It is equipped with a NVIDIA Jetson Nano, Logitech C930e web camera, RP LIDAR S1 and Lightware LW 20/c Laser Rangefinder.}
    \label{fig:quadcopter}
\end{figure}

\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/Vel.png}
    \caption{Plots of the velocity and tracking errors during a high speed flight at maximum speed of 4.5 m/s}
    \label{fig:control_plot}
\end{figure}

\begin{figure*}
	\centering
    \includegraphics[width=0.9\linewidth]{graphics/outdoor2.png}
    \caption{Composite image of the outdoor experiment. The quadcopter must travel from start    to the goal    .}
    \label{fig:outdoor}
\end{figure*}

\subsection{Outdoor Experiments}
Finally, to evaluate the proposed system for high speeds, a custom-built quadcopter shown in Fig. \ref{fig:quadcopter} was tested in an outdoor environment. The quadrotor is composed of TBS 500 Carbon fiber frame. The quadrotor houses an of the shelf ARM-based computer, NVIDIA Jetson Nano, for onboard computation of perception, control, and planning. Pixhawk 4 is the flight controller, which uses the PX4 flight stack. The vehicle has a 35 percent hover throttle on a 5000 mAh 3S Lithium Polymer (LiPo) battery. The quadcopter is equipped with a Logitech C930e web camera.  The camera module packs up with a 1080p resolution, H.264 video compression with Scalable Video Coding and UVC 1.5 encoding to minimize its dependence on computer and network resources and a wide 90-degree field of view. Slamtec RP LIDAR S1 is used for the depth estimation. It provides 360-degree scan field, 5.5hz/10hz rotating frequency with 40-meter ranger distance measurement with more than 9.2K samples per second.
A pole was erected in the middle of a field. The quadcopter was placed at the initial launch position. The destination position was given as an input and the program was run. During the mission the quadcopter was able to avoid the pole in between while operating at a desired velocity of 4.5 m/s. Also the height along z-direction was maintained, i.e, 2 m. An image instance of the same is given in fig. \ref{fig:outdoor}. Fig. \ref{fig:control_plot} shows the commanded velocity and estimated velocity. The experiment shows that FOAM can perform sense and avoid in outdoor environments at a very high speed. Also, it demonstrates the low latency of the proposed FOAM architecture by using it on low cost computing devices.

\section{Conclusion}

In this paper, the novel Fast Obstacle Avoidance Motion (FOAM) algorithm is proposed to perform sense and avoid (SAA) operations. FOAM is a low-latency perception-based algorithm that uses multi-sensor fusion of a monocular camera and a 2-D LIDAR.  It has been shown to perform obstacle avoidance at a speed of 4.5 m/s in outdoor environments. Also, it has been shown to perform high-speed maneuvers in a cluttered environment in both Gazebo and AirSim.

\bibliographystyle{IEEEtran}

\section{Introduction}

{R}{ecent} advancements in Unmanned Aerial Vehicle (UAV) technologies have accelerated the deployment of UAVs for various applications like visual inspection \cite{3c854a3fe5b44f23bf0b8345d8b67db8}, reconnaissance missions \cite{5600072}, agriculture \cite{fe2adf4814da4b9b8c88ab56963bd240} etc. The current increase in humans' reliance on UAVs is due to their mechanical simplicity and ease of control. Therefore, there is a need to develop robust automation algorithms to enhance the capabilities of a UAV to operate in complex environments. In this paper, we present a perception-based algorithm for fast obstacle avoidance motion (FOAM) to enable a UAV to operate at high speed in a cluttered environment.

Many attempts have been made towards the development of robust obstacle avoidance systems. Development of obstacle avoidance algorithms were focussed on coordinated flight and obstacle avoidance with respect to the UAVs crashing into each other \cite{1272530}. One of the popular proportional navigation (PN Law) for missile systems was used on UAV systems \cite{Han2009ProportionalNC}. Also, a lot of work in the field of UAV navigation used a known map of the environment, along with sensor data being streamed to the ground station computers for processing \cite{MANNAR2018480}, thus leading into huge latency issues. Attempts to obtain multi-sensor fusion based obstacle avoidance were made, \cite{Fasano2008MultiSensorBasedFA} used as many as 1 radar, 4 cameras, and 2 onboard computers to achieve, which is not a viable option for commercial usage. Recent advancement in the field of lightweight sensing and computing have led to overcome the above-mentioned problems. Many algorithms using complex local \cite{Daftry2016RobustMF, 7487284, 7989677, Florence2016IntegratedPA} and global \cite{Oleynikova2016ContinuoustimeTO} planners have been designed to solve the obstacle avoidance problems. But these algorithms have also not shown promising results as planning modules in them were constrained due to sensors FOV, as decisions are made on the instantaneous sensor data. Several state-of-the-art approaches for MAV control with impressive aggressive manoeuvres \cite{Tordesillas2019RealTimePW} could be used for real-time applications. But these methods require accurate instantaneous state feedbacks which can only be delivered by motion capture systems, thus unsuitable for the purpose.

Recently researchers have been using fusion of laser scanners and RGB-D cameras for obtaining accurate depth estimates. \cite{Bachrach2012EstimationPA} have used RGB-D sensors to obtain more detailed and faster scans of the environment, but are restricted to very low range in outdoor conditions. Accurate depth estimation of the avoidance of obstacles can be obtained by usage of stereo cameras \cite{8949363}, or by usage of monocular cameras of creation of depth maps using various Simultaneous Localization and Mapping (SLAM) algorithms \cite{Bachrach2009AutonomousFI}, but are computationally intensive for onboard computers and thus provide very low position and velocity feedbacks. \cite{6630809} were able to achieve autonomous flight at a stretch of $3$ km, at a speed of $1.5$ m/s in a forest cluttered environment. Their approach used imitation learning method, which mimics the human learning techniques. Approaches related to learning of motion policies and depth from input data, was demonstrated by \cite{Bills2011AutonomousMF}, where used orthogonal structures of indoor scenes to estimate the vanishing point to navigate the MAV in the corridors by traversing towards dominant vanishing points. A lot of other researchers have applied various learning techniques like imitation learning \cite{Schaal1999IsIL, 6287440, Argall2009ASO} to demonstration the navigation of MAVs, but in most of the cases the MAV streams data to an external computer and receives higher level control commands, which violates the sole purpose of autonomous flight. Hence there is still a need to develop and cost effective, less computationally intensive obstacle avoidance algorithm for the fast manoeuvrability of UAVs in a cluttered environment to perform various jobs like package delivery, spraying of pesticides and fertilizers in agricultural lands, structural inspection etc.

In this paper we propose a new algorithm referred to as Fast Obstacle Avoidance Motion (FOAM) for SAA operations. The proposed perception-action algorithm FOAM is determination of free space by formulating an optimization problem by fusing the POM's obtained from the monocular camera and 2D-LIDAR. FOAM determines the least occupied sector in the 3D frame by using the data obtained from the fused POM's, thus ensuring robust flight to reach the goal. A cheap monocular camera is used to capture images at 30 Hz which then would be processed to obtain the probabilistic occupancy map (POM). A 2D Lidar is also used for the generation of probabilistic occupancy map (POM), and then is fused with the POM generated using camera images. This computes the free space available for the multirotor to traverse in an unknown environment. High-level commands like the desired yaw, lateral velocity etc. are computed and are sent to the Pixhawk autopilot on the multirotor system. The complete architecture is realised in Robot Operating System (ROS) due to its distributed nature.

The performance of the proposed algorithm is evaluated in both simulations and real-time experiments. First, software-in-loop simulations were carried out to verify the performance of the algorithm in a cluttered forest environment. Later, simulations in AirSim simulator were done to realise near outdoor like environment. Finally, the proposed algorithm was tested on a custom built multirotor in a complete outdoor environment.

The remaining paper's organisation is as follows: Section II presents the motivation and the formulation of the problem followed by the proposed algorithm. Section III discusses about the experimental setup and the observation made. Finally, Section IV provides the performance evaluation followed by the conclusion.

\section{Problem Formulation}

The advancement of small-form-factor sensing technology and computing boards motivate researchers to develop a small quadcopter operating in an unknown cluttered environment for search mission. These small quadcopters operate at high speed in such environments. The autonomous operation of these vehicles is challenging due to obstacles like poles, wires, trees, branches, etc. To counter this, one can use deep learning-driven sense-and-avoid approaches (include citation), but they need very high computing power. Also, they have high latency in perception-action. Thus, restricting the endurance and operating speed of small quadcopters. The objective is to develop a low-latency perception algorithm to sense-and-avoid obstacles in an unknown cluttered environment and navigate the fast-moving small quadcopters from its initial launch position $({x}_0 \in \Re^3)$ to goal position $({x}_g \in \Re^3)$. A typical unknown cluttered environment is shown in Fig. 1. The small quadcopter is launched at position $\bf{x}_0$. The 2D LIDAR provides point-cloud data at a radius of $r$, and a monocular camera provides visual sensor information in the heading direction with a 90deg field of view.  In addition to LIDAR and monocular vision, the flight controller provides inertial measurements and position information of the vehicle. Based on the sensing information, the vehicle will identify the obstacle-free region and identify the boundary of obstacles in the sensing region.

From the figure, the vehicle senses the obstacle ($o_1$) partially and generate a feasible path to reach the goal point as shown in the figure.  Here, the vehicle speed restricted between $\bf{v}_{min}$ and $\bf{v}_{max}$ and the height is kept constant.

\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/problemdef.png}
    \caption{A typical unknown clutter environment highlighting the obstacles, launch position, goal position and sensor information. The green circle around the UAV defines the space UAV occupies in the given map. The blue region is the effective sensing zone of the UAV. The regions $O_1, O_2, O_3, …… O_i, …. O_j$ are the obstacles. The dotted line denotes the shortest path for the UAV to traverse from $X_0$ (start point) to $X_g$ (goal point). The yellow path depicts a typical the UAV would follow to avoid the obstacles to reach the goal.}
    \label{fig:feature_ext1}
\end{figure}

The problem of free space determination in the sensing area using camera and LIDAR are shown in Figure 2. Based on the field of view of monocular vision camera Fig 2(a), the width of the image is divided into $m$ sector. Similarly, the 2D LIDAR point cloud data is taken only in the camera field of view. Similarly, the 2D cloud point of LIDAR is selected in the camera field of view as shown in Fig. 2(b). Even though the LIDAR perception can provide 360 field of view, algorithm selects the region of interest as a camera FOV. Hence, LIDAR 2D point cloud data is mapped to $m$ sectors as in camera. The objective is to find the sector which is free from obstacle and also have minimum deviation from the goal point. One need to develop a low latency perception-action algorithm for fast moving small quadcopter operating in an unknown uncertain environment.

\begin{figure}
	\centering
    \includegraphics[width=0.6\linewidth]{graphics/cam_fov.png}
    \caption{Camera field-of-view }
    \label{fig:feature_ext1}
\end{figure}

\begin{figure}
	\centering
    \includegraphics[width=0.6\linewidth]{graphics/lidar_fov.png}
    \caption{LIDAR 2D cloud mapped in camera FOV }
    \label{fig:feature_ext1}
\end{figure}

\section{Fast Obstacle Avoidance Motion (FOAM)}
In this paper, we propose a low-latency perception-action algorithm for high-speed small quadcopter operating in an unknown cluttered environment. The proposed perception-action algorithm is referred as Fast, Obstacle Avoidance Motion (FOAM). The schematic diagram of FOAM system architecture is shown in the Figure 3. The figures show different components of FOAM and the process of integration into the small quadcopter. First, FOAM generates Probability Occupancy Map (POM) from monocular camera at $xz$-plane and POM from 2D-LIDAR at $xy$-plane in the camera field of view. Next, the problem of free space determination is formulated as a optimization problem by fusing the POM's. The solution to the problem finds the free space in the search area. Finally, the local planner generates the necessary command signal to Pixhawk flight controller such that the small quadcopter avoids the obstacle and move towards the goal point. Due to distributed nature of FOAM, we have used ROS framework to realize the FOAM algorithm. Now, we describe working of different components of FOAM.

\begin{figure}
	\centering
    \includegraphics[clip,trim=0mm 0mm 0mm 0mm,width=1\linewidth]{graphics/schematic.png}
    \caption{A Schematic Diagram of FOAM System Architecture}
    \label{fig:feature_ext1}
\end{figure}
This section comprises of the proposed algorithm (FOAM) in the following sub-sections

\subsection{Monocular Camera based Probabilistic Occupancy Map}\label{AA}
The problem of detecting the obstacle from the current frame $t$ is converted to free space determination problem. In order to determine the free space available for the quadcopter to navigate towards the goal, we determine the probability of a sector occupied with an obstacle. For this purpose, RGB image (640 X 480 resolution) is converted to gray-scale image. From the gray-scale image, we have extracted robust corners present in the image using Shi-Tomasi corner detector algorithm \cite{323794}. It is shown in the literature that these features are robust to track in image where the changes are not abrupt. One should note that in a nominal wind condition, the objects present in. the cluttered environment does not move abruptly. Hence, the corner features ($x_i$, $i=1,2,\cdots,N$) detected will be useful to identify the free space present in the current frame. Further, the optical flow values of these sparse features set are computed using Lucas-Kanade method of iterative pyramids \cite{inproceedings}. For this purpose, we have used past three frames to track these corner features. Let $n_i$ be the number of features present in $i$th sector, $v_i$ is the flow for given feature $x_i$ and $N=\sum^m_{i=1}n_i$ be the total number of features with reliable optical flow values in the image frame. Using the flow values of these features, we compute the Probability of Occupancy Map (POM) for $i$th sector as given below,

\begin{equation}\label{Camera_POM}
P_i^c\left(I_t\right)=\frac{\sum_{j=1\ \&\ x_i\ \in S_i}^{n_i}v_j}{\epsilon+\sum_{j=1}^{N}v_j},\ i=1,2,\cdots,m
\end{equation}

Where $\epsilon$ is close to zero. Note that the flow values for the features deducted very far away from the camera will be low and flow values for features near the camera will be high. Hence, POM of sector will be small if the obstacle is far away from the quadcopter. The computational requirement of corner features and optical-flow are small and one achieve 30 frame per second processing speed to deduct the free space.

\subsection{2-D LIDAR based Probabilistic Occupancy Map}
A long range 2-D Lidar is used to estimate the depth information of the environment to the system. The RP LIDAR used in the study provides a 360-degree information with 40 meters effective depth.  In order to estimate the occupancy in the camera plane, the field of view is restricted to camera FOV as shown in Fig. 2(b). The 2D point cloud data is mapped to the camera plane and effective depth is restricted at $d_{max}$. Based on the resolution of LIDAR, one will have finite number of points in each sector.  Note that the depth information of points beyond the desired sensing radius are considered to be of the effective depth. It is set at 10m in the simulation.

The probability occupancy map using 2D point cloud data for the $i$th sector is given as

\begin{equation}\label{lidar_POM}
P_i^L\left(I_t\right)=\frac{\sum_{j=1\ \&\ p_i\ \in S_i}^{n_i}{d_{max}-d}_j}{\epsilon+\sum_{j=1}^{N}{d_{max}-d_j}},\ i=1,2,\cdots,m
\end{equation}

From the equation, we can see that the probability will be zero, if the sector does not have any obstacle.

\subsection{FOAM Optimization Problem}
The probability occupancy map obtained from camera and LIDAR point cloud data are fused as:

\begin{equation}\label{optimisation}
P_i\left(I_t\right)=w_CP_i^C\left(I_t\right)+w_LP_i^L\left(I_t\right),\ i=1,2,\cdots,m
\end{equation}

where $w_C$ and $w_L$ are the weights for camera and LIDAR POM respectively and $w_C + w_L = 1$.

\begin{algorithm}

\caption{Pseudocode of FOAM Algorithm}
\begin{algorithmic}

\Statex
 \State Data: Current image frame $f_k'$, Lidar Data $L$, GPS Data $G$;
 \State $\epsilon$ $\leftarrow$ positive value close to zero;
 \State $H$ $\leftarrow$ empty list;
 \State $P$ $\leftarrow$ empty list;
 \State Algorithm Parameters: $W_C, W_L | W_C + W_L = 2, D_{max}, m$

 \Function{FOAM}{}
    \For{i in $1, 2, ……. m$}
        \State $P_i^C$ $\leftarrow$ POM $image ()$
        \State $P_i^L$ $\leftarrow$ POM $lidar ()$
        \State $P_i$ $\leftarrow$ $W_C P_i^C + W_L P_i^L$
        \State $P$ $\leftarrow$ append ($P_i$)
    \EndFor

    \State $S_d$ $\leftarrow$ minYawCost (P)
    \State currYaw $\leftarrow$ from GPS Data G
\If {$S_d$ $\geq$ 5}
\State yaw $\leftarrow$ currYaw + 10 * (i – 5)

\Elsif {Sd $<$ 5}
\State yaw $\leftarrow$ currYaw – 10 * (5 - i)

\Else
\State yaw $\leftarrow$ currYaw
\EndIf

\EndFunction

\Function{POM image} {}

\State Get $f_k'$ from camera
\State $f_k$ $\leftarrow$ convert $f_k'$ to gray-scale image
\State $X_i$ $\leftarrow$ corner features for sector $S_i$ in $f_k$ \cite{323794}
\State $V_i$ $\leftarrow$ optical flow values for given $X_i$ in frames $f_k– 1$,$f_k – 2$,$f_k-3$ \cite{inproceedings}
\State calculate $P_i^C$ using Equation (\ref{Camera_POM})
\State return $P_i^C$

\EndFunction

\Function {POM lidar}{}

\State Get $L$ from lidar
\State $D_j$ $\leftarrow$ lidar data in sector $S_i$ in frame $f_k, D_j \in L$
\State calculate $P_i^l$ using Equation (\ref{lidar_POM})
\State return $P_i^L$
\EndFunction

\Function {minYawCost (P)}{}

\State $P_{min}$ = min(P)
\For {l in 0, 1, ……. 8}
\If {P = $P_{min}$}
\State H $\leftarrow$ append (l + 1)
\State return H
\Else
\State return min (abs (h – 5)) $\forall h \in H$
\EndIf
\EndFor
\EndFunction

\end{algorithmic}
\label{Algo}
\end{algorithm}

\subsection{Flight Control}

\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/control.png}
    \caption{A Schematic Diagram of Flight Control}
    \label{fig:feature_ext1}
\end{figure}

The algorithm is designed to send high-level commands to the quadrotor – (1) forward translational speed, (2) constant height, and (3) rotational velocity (heading/desired yaw). The forward velocity of the complete system is constant throughout the whole mission and thus can be modified by the user based the application of the vehicle. Figure 4. Shows the schematic diagram of the flight control used for this algorithm. As mentioned in the FOAM optimization problem, the required high-level commands like the desired yaw, height of the vehicle etc. are sent to the velocity controller present in the Pixhawk autopilot. These commands are sent via MAVLINK and MAVROS to the autopilot. The Pixhawk is connected to the on-board computer using a USB – ttl serial connector, which is connected to the Serial Port of the Pixhawk at a fixed baud rate of 921600. PID controller is used in the velocity controller. The velocity controller computes the desired thrust, roll, pitch and yaw and sends it to the attitude controller. The PID controller of the attitude controller then realises the required motor speed on the quadrotor for the optimal motion. The above-mentioned process is completely realised inside the quadrotor Pixhawk autopilot.

\subsection{FOAM Algorithm}
The proposed perception-action algorithm Fast, Obstacle Avoidance Motion (FOAM) is determination of free space by formulating an optimization problem by fusing the POM's obtained from the monocular camera and 2D-LIDAR. FOAM determines the least occupied sector in the 3D frame by using the data obtained from the fused POM's, thus ensuring robust flight to reach the goal. Algorithm 1. shows the FOAM Algorithm

\begin{figure*}[h]
\centering

    \subcaptionbox{Simulation frame at time 26 \textit{seconds}\label{fig:a2}}
    {\includegraphics[clip,trim=0.5mm 0.5mm 0mm 0mm,width=0.33\textwidth, height=4cm]{graphics/forest1.png}}
    \subcaptionbox{Simulation frame at time 32 \textit{seconds}\label{fig:b2}}
    {\includegraphics[clip,trim=0.5mm 0.5mm 0mm 0mm,width=0.33\textwidth , height=4 cm]{graphics/forest2.png}}
    \subcaptionbox{Simulation frame at time 86 \textit{seconds}\label{fig:c2}}
    {\includegraphics[clip,trim=0.5mm 0.5mm 0mm 0mm,width=0.32\textwidth , height=4 cm]{graphics/forest3.png}}

        \subcaptionbox{Experiment frame at time 34 \textit{seconds}\label{fig:d2}}
    {\includegraphics[clip,trim=1.2mm 1mm 0mm 0mm,width=0.33\textwidth, height=4cm]{graphics/forest4.png}}
    \subcaptionbox{Experiment frame at time 41 \textit{seconds}\label{fig:e2}}
    {\includegraphics[clip,trim=1.2mm 1mm 0mm 0mm,width=0.33\textwidth , height=4 cm]{graphics/forest5.png}}
    \subcaptionbox{Experiment frame at time 91 \textit{seconds}\label{fig:f2}}
    {\includegraphics[clip,trim=1.2mm 1mm 0mm 0mm,width=0.32\textwidth , height=4 cm]{graphics/forest6.png}}

    \caption{Different instances of a AirSim Forest Simulation. The quadcopter flies towards Goal point while avoiding the obstacles.}
    \label{fig:sim_exp_trajectory2}
\end{figure*}

\section{Performance Evaluation}
In order to evaluate the performance of FOAM, different test scenarios are considered.Custom environments were created and tested using Gazebo simulator using PX4 Flight Stack in the back-end for the 3DR IRIS quadcopter. Simulations were done on AirSim to verify the performance of the algorithm. Finally, to evaluate the entire proposed system, experiments were conducted in outdoor environment using a custom-built quadrotor vehicle which would autonomously navigate based on the high-level commands it receives from the onboard computer.
\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/gazebo_com.png}
    \caption{Bugtrap (left) and Town (right) environments used in Gazebo simulator.}
    \label{fig:gazebo}
\end{figure}
\subsection{PX4 ROS Gazebo Simulations}
In order to test the proposed algorithm custom environments were designed.The simulator uses a python custom code for providing control commands to the 3DR IRIS quadrotor which has a monocular camera and 2-D lidar plugins. The monocular camera uses a horizontal field of view of $90$\degree and sensing range of $10$ m for the 2-D lidar. The sample Gazebo environment are shown in Fig 7

\subsection{AIRSIM Simulation}
Next, the algorithm was tested it on AirSim, an open source simulator; due to the algorithms distributed nature i.e. ROS framework, MAVROS is used to integrate the PX4 flight stack. The quadcopter model uses a built-in monocular camera along with the 2D LIDAR for perception. Different instances in Fig. 9 shows a typical forest environment in an AirSim simulator. The algorithm was also tested on a custom designed cluttered environment in AirSim to test its robustness. The simulation was carried out using Intel i7 8750H 3.0 Ghz processor, powered with 16GB of DDR4 memory which also housed an NVIDIA RTX 2070 Max-Q graphics memory.

\subsection{Outdoor Experiments}
Finally, in order to evaluate the proposed system for high speeds a custom-built quadcopter shown in Fig. 10. was tested in an outdoor environment. The quadrotor is composed of TBS 500 Carbon fiber frame. The quadrotor houses an of the shelf ARM based computer; NVIDIA Jetson Nano for onboard computation for perception, control and planning. Pixhawk 4 is the main flight controller, which uses PX4 flight stack. The vehicle has a 35 percent hover throttle on a 5000 mAh 3S Lithium Polymer (LiPo) battery. The quadcopter is equipped with an off the shelf Logitech C930e web camera.  The camera module packs up with a 1080p resolution, H.264 video compression with Scalable Video Coding and UVC 1.5 encoding to minimize its dependence on computer and network resources and a wide 90-degree field of view. Slamtec RP LIDAR S1 is used for the depth estimation. It provides 360-degree scan field, 5.5hz/10hz rotating frequency with 40-meter ranger distance measurement with more than 9.2K samples per second.
\begin{figure*}
	\centering
    \includegraphics[width=0.9\linewidth]{graphics/outdoor2.png}
    \caption{Composite image of the outdoor experiment. The quadcopter must travel from start    to the goal    .}
    \label{fig:feature_ext1}
\end{figure*}

\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/uav1.png}
    \caption{Custom UAV used in the experiments. It is equipped with a NVIDIA Jetson Nano, Logitech C930e web camera, RP LIDAR S1 and Lightware LW 20/c Laser Rangefinder.}
    \label{fig:feature_ext1}
\end{figure}

To evaluate the performance of the proposed algorithm in outdoor/real-time condition a pole was erected in the middle of an empty field. The quadrotor is fed with initial and final positions. Fig shows the commanded velocity and estimated velocity. During the complete mission the quadrotor is able to achieve the desired velocity, maintain the given height and perform required manoeuvres to avoid the obstacle.

\begin{figure}
	\centering
    \includegraphics[width=1\linewidth]{graphics/Vel.png}
    \caption{Plots of the velocity and tracking errors during a high speed flight at maximum speed of 4.5 m/s}
    \label{fig:feature_ext1}
\end{figure}

\section{Conclusion}
The work presented is FOAM, is a low-latency perception-action algorithm for high-speed small quadcopter to operate in unknown environments.The FOAM was tested successfully in both simulated and in hardware flights, achieveing velocities up to $4.5$  m/s.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


It's all about technique. Chef John McNabb is eager to flash his talents on the grill for this wine dinner. Understanding cooking methods are vital to serving flavorful dishes, the temperatures and times of each main ingredient will be carefully watched and slow cooked when appropriate. The results will be moist foods with deeper flavors and charred tastes. Farm to Sea means seasonal vegetables and local meats combined with the freshest seafood available. This is a five-course meal paired with premium wines from Knapp Winery, Chateau LaFayette Reneau Wine Cellars, Zugibe Vineyards, and Glenora Wine Cellars. Please purchase your tickets online to reserve your spot $82.50, includes tax and gratuity. Also included is a sparkling wine reception before the meal!


text:
Former Staff File Lawsuits against Adventist Hospitals in Florida Alleging Billing Fraud
by AT News Team
Former employees of several hospitals in Florida owned by the Adventist Health System have filed lawsuits under the "whistleblower" law, according to stories in the Orlando Sentinel. Marian Wilbanks, an attorney specializing in this kind of lawsuit represents these individuals and told the newspaper that they involve "tens of millions of dollars" in overbilling Medicare, Medicaid and Tricare, Federal health benefits programs.
These are not criminal cases filed by Federal prosecutors but civil suits filed under provisions in the law that allow employees or others who observe excess billing to be paid a part of the financial settlement in cases where the court decides that the allegations are true. The first lawsuit was filed in July 2010 and additional complaints were added in October this year.
The lawsuits make four accusations: (1) Improper coding was used in invoices for radiology services to the three Federal programs from 1995 to 2009. (2) Invoices were sent for larger doses of the drug octreotide, used to enhance MRI scans, than were actually administered. (3) Invoices were sent for computer-aided analysis that was never done. (4) Invoices for emergency services were routinely submitted that were "false, duplicate or padded" from 2001 to 2008.
The newspaper has identified two individuals as filing litigation. Amanda Dittman worked as a compliance specialist in Florida Hospital's billing department from 2001 to 2008 and from 1996 to 2001 for Florida Radiology Associates. Dr. Charlotte Elenberger is a radiologist affiliated with Florida Radiology Associates since 1995.
"I saw upcoding happening daily in heavy volumes across seven hospitals," Dittman told the Orlando Sentinel. She reported the problems to her superiors, she said. The health system "conducted an internal investigation that confirmed double- and overbilling were occurring, the suit alleges, but did not correct the problem or refund the money," the newspaper stated.
The health system has twice filed motions with the court to dismiss the litigation. "The first motion … was denied by U.S. District Judge John Antoon, who called the evidence 'extensive and sufficient,'" the newspaper reported. The second motion to dismiss has not been ruled on.
"Florida Hospital takes seriously our obligations under the law," said Samantha O'Lenick for the health system. "If we discover mistakes, we take appropriate action to correct them. … However, we believe the recent allegations … are asserted to achieve settlement pressure in … ongoing litigation [and] are without merit." Neither Dittman nor Elenberger had anything to do with emergency department billing, O'Lenick told the Sentinel.
Adventist Today has been told by an attorney familiar with health care litigation that legal action related to billing is used by the Federal government to reduce the cost of health care and pressure hospitals into returning sums of money. It is usually not a criminal matter and the facts involved in these cases are very complex.
Florida seems to be a center for insurance billing fraud. Late this year a group of physicians and providers in south Florida were indicted and convicted of several million dollars of ovebilling and outright fraud for procedures and medical items.
If the fraud in Medicare could be curtailed, it means a large reduction in medical costs now being incurred and with the changes and new additions coming with the ACHA, it is absolutely essential that these cases be vigorously pursued. We all pay for fraud.
Aha, the worm in the apple has been revealed. This shouldn't be a revelation to anyone having medical services performed in the past 40 years. Every hosp. med. bill we ever received when covered by private insurance, we were able to reduce by an average of 30%, when we questioned the charges. (this included one, w surgery & recup. at a SDA associated facility. With many of us now in HMO's, we never see the item by item charges.
i submit this is all a farce, that the Govt. always knew what was going on. Every one was in on the payout. The AMA, pharma, research, insurance co's, MD's,were enriching the whole system with the $billions annually with their lobbyists & exorbitant insurance fees. Not saying every MD was involved directly, but were, thru their associations membership. No doubt the majority with private practice were involved as they utilized software systems which detailed what the new norms were. Some probably assumed this was known by Govt, which ignored the felonious practice.It permitted them to improve their services by upgrading to the latest equipment & systems.
In as much as the whole medical system has utilized this corrupt practice, every one involved has benefitted greatly, the politicos, the associations, insurance co's, hospitals, pharma co's, mfg'ers, MD's. A few may be offered up as scapegoats, but it will be just the tip of the iceberg. Canada instituted a compulsory Federal medical program 40 years ago. It appears to be working well for most. i have children there. The only complaint i've heard is sometimes a wait for some surgery needs. Don't know if there are less or curtailed services for the aged. i personally know 3 MD's who had good practice's in Canada, who left for the US when the Canada-wide Federal program was enacted.
i have no doubt the new Health care plan in the US will still have similar loop holes which will permit
coruption & payouts to continue, with the same beneficiaries ripping off the ever loving taxpayer.


text:
Family Call For Review Of NHS Recruitment Procedures To Prevent Future Needless Suffering
Serious Failings Highlighted In Treatment
The brother of a man who 'suffered needlessly' after a medical expert repeatedly mistook an aggressive cancer for a sore throat has spoken of his anger after learning that the hospital didn't even interview the locum consultant in charge of his care before appointing him.
Gordon Thornton, 60, died after months of painful treatment for the fatal tumour that had developed in his throat. And his family believe that the errors made by Dr Gomez-Estancona prevented Gordon accessing the care and pain relief he so desperately needed.
Now Gordon's brother Dr Everard Thornton is calling for more stringent checks to be put in place before locum doctors are allowed to treat patients after receiving an apology from the Macclesfield General Hospital and an admission that they failed in Gordon's care, and learning that others had also suffered at the hands of the 'rogue doctor'.
Medical law and patient's rights experts from Irwin Mitchell, who represented the family in their battle for justice, confirmed that following the tragic death of Gordon an investigation carried out by the GMC in August 2010 found serious failings in his treatment, which saw conditions imposed on Gomez-Estancona's registration
The doctor, whose colleagues had expressed 'great concerns' about his ability to treat patients during his time at the hospital, particularly in relation to his language and communication skills, was suspended altogether following a further GMC investigation earlier this year that looked into the cases of four other patients he'd treated.
Gordon's family, who are from the Wirral, say justice will only be done when the NHS demonstrates that lessons have truly been learnt and more stringent recruitment processes put in place after it came to light that a Consultant in ENT at Macclesfield Hospital admitted he found Dr Gomez-Estancona via an agency and had considered his CV but didn't interview him or check his competence prior to his appointment.
In 2006 father of one Gordon was referred by his GP to see a specialist as he had a persistent sore throat and a painful history of earaches. There had been a noticeable change in his voice tone, and suffering from catarrh was a cause for concern.
But ENT specialist Dr Gomez-Estancona detected no problems, and Gordon was prescribed a mouthwash and sent home only to return to the hospital for an MRI scan of his ears at a later date. But his pain and symptoms were so extreme that within days he was forced to return to his GP, pleading for help.
Sadly, after further tests and hospital visits, seven months later he was diagnosed with cancer of the throat. By this stage it was too advanced to be cured and he was given palliative care until he passed away in August 2008.
The family have since received a five-figure out of court settlement from the East Cheshire NHS Trust March 2011 which is responsible for Macclesfield General Hospital, and an apology from the Deputy Chief Executive.
Leena Savjani from Irwin Mitchell said: "This was never about the money for the Thorntons, but about making sure no other family will ever have to go through what they have.
"Sadly, by the time Gordon was diagnosed with cancer, the tumour in his throat was so large he could no longer swallow, speak and breathe properly, and had a tube to help him breathe and speak inserted in his throat (tracheostomy) as well as a feeding tube inserted into his stomach.
"Although we will never know for certain whether the outcome for Gordon may have been different if a diagnosis had been made earlier, his pain and suffering during the final months of his life could certainly have been reduced and palliative care could have begun sooner."
Leena continues: "As a firm we are determined to continue to fight on behalf of our clients to ensure patient safety is always a top priority – Gordon's case and those of the additional four patients who suffered at the hands of Dr Gomez-Estancona - highlight the very real need to extend this to people working for hospital Trust's temporarily and that rigorous checks about suitability for employment are in place."
Commenting on his family's ordeal, Gordon's brother, Dr Everard Thornton said: "It seems unbelievable to me and my family that there was a failure to interview a consultant.
"I appreciate that this was likely a rogue doctor and that most medical staff do their job, and do it well. But the fact remains that my brother suffered unnecessarily as a result of the treatment he received.
"It is imperative that the NHS learn from what happened to him, that more stringent measures are put in place to protect patients, especially when there is a possibility of communication difficulties. Surely things like this can't be judged simply by looking at a CV.
"Whilst we have received an apology from the East Cheshire Hospital Trust and are pleased that Dr Gomez-Estancona has been suspended, I want assurances that steps have been taken to put recruitment processes right within all hospitals, so that no other families suffer in the way we have.
"I also want to urge doctors to request repeat tests if they feel symptoms of a patient remain consistent with a condition, and to do it quickly, even if the all clear has been given by a specialist at a hospital. As was shown by my brother's case, consultants can make costly mistakes and are not always right when it comes to making a diagnosis."
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Dragonflies buzz over deep blue water that is surrounded by greenery and colourful flowers. Sounds like a sleepy, warm summers evening in the country.
Well we can't all be there all the time but you can have a little slice of the pond in your living room or bedroom.
This tasteful and petite tiffany lamp depicts a colourful vista and sits atop a cold cast bronze stand. The single globe is operated via a cord switch. The compact size of this table lamp means you can easily use it as a bedside lamp or in any other living area where space is at a premium.


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Tenmile Lake
Tenmile Lake is the largest and southernmost of a chain of lakes along the Oregon Coast south of the Umpqua River in the United States. The chain includes North Tenmile, Eel, Clear, and smaller lakes, which drain into the Pacific Ocean via Tenmile Creek.
Localities in the Area
Lakeside is a city in Coos County, Oregon, United States. Lakeside is situated 3 miles northwest of Tenmile Lake. Photo: Visitor7, CC BY-SA 3.0.
Tenmile is an unincorporated community in Coos County, Oregon, United States. Tenmile is situated 4 miles northwest of Tenmile Lake.
Hauser is an unincorporated community in Coos County, Oregon, United States. Hauser is situated 6 miles southwest of Tenmile Lake. Photo: Finetooth, CC BY-SA 3.0.
Type: Lake
Description: lake in Coos County, Oregon, USA
Category: lake
Location: Oregon, Pacific Northwest, United States, North America
43.55° or 43° 33' 0.1" north
-124.1234° or 124° 7' 24.3" west
84MQHV2G+2J
13 feet (4 metres)
Cebuano: Tenmile Lake (lanaw sa Estados Unidos, Oregon)
Cebuano: Tenmile Lake
French: Tenmile Lake
South Tenmile Lake
Ten Mile Lake
Other Places Named Tenmile Lake
Ten Mile LakeLocality in British Columbia, Canada
Ten Mile LakeVillage in Nova Scotia, Canada
Ten Mile LakeLake in Livingston County, Michigan
Tenmile LakeLake in Minnesota, United States
Tenmile LakeLake in Montana, United States
Tenmile LakeLake in Aroostook, Maine
TempletonHamlet
Saunders LakeHamlet, 5 miles west
ShorewoodHamlet, 8 miles southwest
GlasgowHamlet, 9 miles southwest
Templeton ArmBay
Coleman ArmBay, 1 mile north
School Land BayBay, 1¼ miles north
Willow PointCape, 1¼ miles northwest
Johnson CreekStream, 2,900 feet southeast
Robertson CreekStream, 1 mile east
Benson CreekStream, 1¼ miles northeast
Copple CreekStream, 1½ miles northeast
Mud LakeLake in Minnesota, United States
Lac KawakikamacikLanaudière, Quebec
O'Neal LakeLake in South Dakota, United States
Lac RhéaumeLake in Quebec, Canada
Hawkins PondLake in Vermont, United States
Christie LakeLake in Ontario, Canada
Popular Destinations in Oregon
Portland Oregon Coast
Willamette Valley Salem
HondurasCentral America LisbonPortugal
SarajevoBosnia and Herzegovina JaisalmerRajasthan, India
About Mapcarta. Thanks to Mapbox for providing amazing maps. Text is available under the CC BY-SA 4.0 license, excluding photos, directions and the map. Based on the page Tenmile Lake. See this page's contributions. Photo: Wikimedia, CC BY-SA 3.0.


Leaders, assistants and helpers in all sections are the people who make Scouting happen for young people. Though your first line of support lies in your District, the Region is also available to offer advice and assistance. The Info section contains useful material and more links.
The Scottish Campsite Directory has been updated (April 2018) and is a useful resource. The UK directory, which lists some Scottish sites, is also available at UK Scout Campsite Directory .


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Friendship & Support
The WMRAC is a Club that provides Friendship and Support for women involved in Motorsport. Working, volunteering and competing ; we provide a network of social interaction, practical help, information and advice to support you along the way.
Founded in 1962 by wives of racing legends, bearing the names Chapman, Cooper, Grant, Hill and Web the Club set-out to provide a place to meet, on-going friendship and support, and in particular support to those in need in the event of tragedy, through Charitable support and the Benevolent Fund.
Our support network and membership is open to all women involved in motorsport through whatever discipline -including the roles of timekeeper, mechanic ,manager, engineer, driver, marshal, paramedic, doctor, tea maker and all-round supporter – encouraging husbands and 'young guns' to join the industry.
As an international Club we reach into all sectors of the industry and work with Racing Clubs and Teams from the grass roots of motorsport up to the heady heights of Formula One.
If you are a woman involved in motorsport, then please get in touch and join our network.
Every year, the WMRAC is proud to support three charities chosen, through an extensive selection process, from great causes nominated by our members.
HOW TO GET IN TOUCH: Contact us via email,
follow-us on twitter or instagram and connect via facebook