Flocks of birds, schools of fish, and colonies of ants exhibit a remarkable robustness and resilience, despite the limited capabilities of each individual. Recently, research into bio-inspired swarm robotics has been gaining popularity due to the low-cost, robust, redundant, and distributed nature of swarms. Potential applications for robot swarms include search and rescue, construction, and chemical spill cleanup, as well as nano-medical applications such as finding tumors. Many of these applications would benefit from simple, cheap, disposable swarms of robots that can accomplish these tasks quickly and without much human supervision.
While there has been a lot of work on different swarming algorithms and technologies, many still require localization, mapping, complex coordination algorithms, and precise identification of neighboring robots’ orientations and relative positions. This often results in swarm behaviors that are interesting, but extremely difficult to implement on actual robotic platforms. For swarm applications in the nano-medical field, developing collective behaviors that use extremely simple controllers and sensors is especially important if these behaviors have a hope of being implemented on nano-robots.
Prior work has shown that swarms of robots so dumb that they have no computational power — they can’t even add or subtract, and have no memory — can still collectively solve canonical multirobot problems such as aggregation and simple object clustering. There are several key benefits to researching the capabilities of extremely dumb robot swarms: 1) the dumber the robot, the cheaper and more disposable it is; 2) the simpler the control algorithm, the easier to implement on real robots; and 3) even teams of smart robots may need a “Plan B” consisting of simple robust algorithms that require only the most basic capabilities in case of malfunction and failure.
This work demonstrated that many interesting behaviors can be achieved using swarms of computation-free robots. It starts with a simple robot model and adds a form of stigmergic control by changing the environment to expand the possible behaviors, and control collective behaviors. A genetic algorithm approach was used to design these swarm behaviors by first defining a fitness function that describes a desired collective behavior, and then searching the space of simple controllers that best achieves this behavior. Three behaviors are possible using very simple sensors and controllers: forming a perimeter, rendezvous, and foraging.
In perimeter formation, the controller that is evolved forms a perimeter around the target. This results in the robots aggregating to the target and forming a perimeter around the target. Experiments were conducted with changing the location of the target mid-simulation. The robots converge to the target and form a perimeter. When the target is placed in a new location, the entire swarm quickly moves to the new location and reforms the perimeter. This behavior is very robust and is automatic — the robots are purely reactive so they can be controlled simply by changing the environment, removing the need to broadcast information to the swarm or have additional control logic.
Another desired behavior was for every member of the swarm to gather as close as possible to the target, rather than just circling around it. Rendezvous is an important behavior for swarms because it sets the stage for more complicated behaviors by assembling a group of robots to a specific desired location. A controller was chosen for the rendezvous problem using a fitness function; however, all trials resulted in controllers in which robots would form a circle around a target.
In foraging behavior, objects and robots are distributed randomly throughout the environment, and the robots must gather the objects to a specified target location. The foraging behavior occurs when one or several fixed targets is placed in the environment. In the classic foraging problem, there is a “nest” location where all of the items must be gathered. In an alternative foraging scheme, multiple stationary targets are placed in the environment. The convex hull of these targets defines the region into which the objects will be harvested. Similar to the previous behaviors, the foraging behavior can be controlled simply by changing the location of the targets. The robots will then move the items to the new desired location.
This work demonstrated that swarms of robots that can’t compute can perform complex behaviors such as rendezvous to a desired location, simple perimeter monitoring of a desired location, and foraging in changing environments. The results show that complex behaviors can be evolved from simple interactions between agents, and that these behaviors can be controlled during execution by simply changing the environment. These behaviors are so simple that they could be hardwired, requiring no computational capabilities. This research is an important step towards swarm behaviors that can be easily implemented in hardware and produced at small, or even nano-scale.