We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the \textit{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 m^3 arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.
@article{arxiv.1909.05150,
title = {Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning},
author = {Carlos E. Luis and Marijan Vukosavljev and Angela P. Schoellig},
journal= {arXiv preprint arXiv:1909.05150},
year = {2020}
}