Extending boids for safety-critical search and rescue

Abstract

Robot swarms can accomplish complex tasks, and in this work, we seek to design swarm robotic algorithms for search and rescue that are scalable to large swarms, efficient in terms of computations, safe from collisions, and tunable to mediate the trade-off between exploration and exploitation in the search. We propose extending the Boids algorithm to accomplish this. Without modifying the three Boids rules of alignment, cohesion, and separation, we add target-seeking and general collision avoidance by using ghost boids . Additionally, we use a control barrier function to improve safety at the cost of increased computation. Via simulation in a search and rescue task, we analyze the trade-offs between safety, computational efficiency, and coverage of the environment for our algorithm. • We extend the Boids algorithm to include collision avoidance using barrier functions. • Through simulations, we analyze performance trade-offs in a search and rescue task. • We trade off exploration and exploitation via a target strength parameter.