Online trajectory generation with distributed model predictive control for multi-robot motion planning

Published in Robotics and Automation Letters (RA-L) / ICRA, 2020

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). https://www.dynsyslab.org/wp-content/papercite-data/pdf/luis-ral20.pdf

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 wellstudied 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 m3 arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.