Learning NEAT Emergent Behaviors in Robot Swarms
This addresses the challenge of controlling robot swarms for complex tasks, but it is incremental as it applies existing evolutionary methods to new robotic platforms.
The authors tackled the problem of learning individual policies for robot swarms to produce desired emergent group behaviors, using an evolutionary algorithm to train behaviors in simulations of aerial and ground robots, achieving effectiveness in tasks like area coverage and wall climbing compared to designed policies.
When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. However, the task of learning an individual policy to produce a desired group behavior remains a challenging problem. We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in animals, we use an evolutionary algorithm to train a population of individual behaviors to produce a desired group behavior. We perform experiments using simulations of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics platforms conducted in the CoppeliaSim simulator. Additionally, we test on simulations of Anki Vector robots to display our algorithm's effectiveness on various modes of actuation. We evaluate our algorithm on various tasks where a somewhat complex group behavior is required for success. These tasks include an Area Coverage task and a Wall Climb task. We compare behaviors evolved using our algorithm against designed policies, which we create in order to exhibit the emergent behaviors we desire.