You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Survey on Coverage Path Planning with Unmanned Aerial Vehicles 
Chapter Title:  Coverage with Uncertainty 
1. Abstract of Cited Paper (BIB001):  This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters. 
2. Abstract of Cited Paper (BIB002):  In this paper, we propose strategies for merging occupancy probabilities of target existence in multi-UAV co- operative search. The objective is to determine the impact of cooperation and type of information exchange on search time and detection errors. To this end, we assume that small-scale UAVs (e.g., quadrotors) with communication range limitations move in a given search region following pre-defined paths to locate a single stationary target. Local occupancy grids are used to represent target existence, to update its belief with local observations and to merge information from other UAVs. Our merging strategies perform Bayes updates of the occupancy probabilities while considering realistic limitations in sensing, communication and UAV movement—all of which are important for small-scale UAVs. Our simulation results show that information merging achieves a reduction in mission time from 27% to 70% as the number of UAVs grows from 2 to 5. 
3. Abstract of Cited Paper (BIB003):  In this paper, we present an algorithm called Learning-based Preferential Surveillance Algorithm (LPSA), which is specific to the problem of persistent surveillance by unmanned aerial vehicles. The novelty of the algorithm lies in providing the capability to a UAV to increase the probability of target detection through learning. The algorithm considers the neighborhood of a localized target as potential additional targets and motivates the UAV to verify if this is indeed true. Once a target is located in any of the neighborhood grid points, that point is visited more often than other points. Moreover, this technique uses the risk calculation method of an existing geometric reinforcement learning algorithm to reduce the frequency of visits to risky regions. Simulation results are used to demonstrate the effectiveness of the algorithm. 
4. Abstract of Cited Paper (BIB004):  In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate adaptive plans according to information-theoretic objectives, enabling the UAV to gather data efficiently. We validate our approach in simulation by comparing against existing methods, and study the effects of different planning strategies. Our results show that the proposed algorithm builds maps with over 50% lower entropy compared to traditional “lawnmower” coverage in the same amount of time. We demonstrate the planning scheme on a multirotor platform with different artificial farmland set-ups. 
5. Abstract of Cited Paper (BIB005):  Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional "lawnmower" coverage. We demonstrate proof of concept using a multirotor to map color in different environments. 
6. Abstract of Cited Paper (BIB006):  A heuristic learning algorithm is presented in this paper to solve the problem of persistent preferential surveillance by an Unmanned Aerial Vehicle (UAV). The algorithm helps a UAV perform surveillance as per quantitative priority specifications over a known area. It allows the specification of regional priorities either as percentages of visitation to be made by a UAV to each region or as percentages of surveillance time to be spent within each. Additionally, the algorithm increases the likelihood of target detection in an unknown area. The neighborhood of a detected target is suspected to be a region of a high likelihood of target detection, and the UAV plans its path accordingly to verify this suspicion. Similar to using the target information, the algorithm uses the risk information to reduce the frequency of visits to risky regions. The technique of using risk map to avoid risky regions is adapted from the existing geometric reinforcement learning technique. The effectiveness of this algorithm is demonstrated using simulation results.

Literature Review:
A waypoint planning algorithm for surveillance is presented by Lim and Bang BIB002 . The area of interest has the form of a hexagonal grid and contains Information Points (IP), as shown in Figure 26a . Each IP works with a certainty value that quantifies the information confidence. When the certainty value is significant, the IP contains trustworthy information and the exploration in the surrounding areas is not necessary. However, this certainty drops along the time with the absence of observations. Lower values denote poor information and demand a new observation in the location. The algorithm uses cost functions such as certainty, distance and interest level to guide the vehicles and select the next points to be explored. In a previous work BIB001 , the authors present details about the cost functions. Regarding the certainty cost function, it is necessary to consider the average certainty of neighboring IPs, guiding the vehicle not only to the point but to the region with the lowest certainty. The A* algorithm is employed to generate the path to reach the next point chosen using cost functions. As a cooperative surveillance mission, a sub-region is assigned to each vehicle to avoid coverage overlapping. Khan et al. BIB003 presented an approach for merging distributed information in cooperative search. The multiple UAVs have several restrictions regarding the sensing capabilities and information trading. Besides, false alarms with a certain probability are also considered by the sensors. The mission consists of a group of small-scale UAVs searching for target locations within a rectangular area discretized into square cells. The vehicles can perform motions in four different directions or remain in the present location. The individual vehicles can sense the area and update the search map, while sharing local information with each other. Fusing their local maps, the aerial vehicles are capable of speeding up the search, while improving the sensing capabilities in relation to approaches without a cooperative strategy. Popović et al. BIB005 introduced an Informative Path Planning (IPP) for weed detection in precision agriculture using UAVs. The authors use a fixed-horizon approach for adaptive planning, alternating between plan execution and replanning. They employ a two-stage replanning, where the 3D path of an aerial vehicle is constantly refined, while respecting motion restrictions. This optimization is obtained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The authors present a multi-resolution IPP for terrain monitoring with aerial platforms BIB006 . The proposed approach is built upon the method established in the previous work BIB005 . But instead of a binary classification of weed occupancy, the method focuses on biomass monitoring. The method builds a probabilistic map with all the visual data gathered in flights at distinct altitudes. The method proposed by Popović et al. BIB005 and further explored by Popovic et al. BIB006 is evaluated in simulation with respect to the back-and-forth and the state-of-the-art IPP algorithm. The authors also run their IPP approach in an AscTec Pelican UAV, emulating the weed classifier through the use of AR tags BIB005 and on a DJI Matrice 100, mimicking the vegetation monitoring on painted green sheets BIB006 . A learning-based algorithm for persistent surveillance problem using UAVs is addressed by Ramasamy and Ghose BIB004 . The problem can be described as a continuous CPP, where the UAV should explore all the locations of an certain area from time to time, while minimizing the interval between those visits. The problem may be more complex considering the necessity of increasing or reducing the frequency of visits depending on the zone profile-an interesting one or a risky one. Ramasamy and Ghose BIB007 extend their work dedicating more attention to the preferential surveillance in a known area. They explore an approach considering different ways of quantitative priority specifications. Besides, they present further simulation results to support a more detailed analysis.