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arXiv · 1909.05150

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

Carlos E. Luis·Marijan Vukosavljev·Angela P. Schoellig
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Abstract

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.

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Carlos E. Luis, Marijan Vukosavljev, Angela P. Schoellig. 2020-01-24. Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning. https://doi.org/10.1109/lra.2020.2964159

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