Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning
This work addresses efficient and safe motion planning for multi-robot systems, such as drone swarms, with incremental improvements in collision avoidance.
The paper tackles real-time trajectory generation for multiple robots using a distributed model predictive control algorithm, achieving a 50% reduction in travel time compared to the Buffered Voronoi Cells method and over 90% success rate with 30 drones in a dense environment.
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.