Online Trajectory Generation With Distributed Model Predictive Control for Multi-Robot Motion Planning

We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the on-demand collision avoidance method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50 of the travel time required to complete a multi-agent point-to-point transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90 success rate with 30 palm-sized quadrotor agents in a 18$\text{m}<^>3$ arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.