Onboard Predictive Flocking of Quadcopter Swarm in the Presence of Obstacles and Faulty Robots

Achieving fluent flocking, similar to those observed in birds and fish, on robotic swarms in a desired direction while avoiding obstacles using onboard sensing and computation remains a challenge. In a previous study (Onur et al, Proc. of ANTS'2022), we proposed a predictive flocking model as a computationally efficient method to generate smoother and more robust motion of the swarm. In this study, we extend this model to achieve safe flocking in cluttered environments in the presence of faulty robots that get immobilized during flocking. Systematical evaluation of the model in simulation with different swarm sizes and different faulty robot ratios has shown that safe flocking can be achieved even when 40% of the robots malfunction during flocking. Finally, we validate the model on a swarm of five micro quadcopters using only onboard range and bearing sensors and computation in a distributed manner without any communication(1).