Fast Generation of Collision-Free Trajectories for Robot Swarms Using GPU Acceleration

As the capabilities of robots and their control systems improve, we see an increasing number of use cases where the simultaneous operation of robots within a space is advantageous. Although trajectories for individual robots can be computed quickly using the existing methods, when robots operate simultaneously and in close proximity, the requirement for collision avoidance introduces a coupling between robot trajectories and makes the trajectory generation problem difficult to solve quickly. In this paper, we propose a parallelizable formulation of such problems and a method for solving them quickly on modern graphics processing units, using momentum-based gradient descent. We demonstrate the proposed framework in simulation using two case studies: a swarm of 200 quadcopters traversing a maze and a fleet of 100 bicycle robots changing their formation. In both the cases, our method requires just seconds to generate feasible, collision-free trajectories for the entire swarm.