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

09/11/2019
by   Carlos E. Luis, et al.
0

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 in the system. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50 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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset