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

TL;DR

This paper introduces a distributed model predictive control algorithm for multi-robot trajectory planning that improves efficiency and success rates in dense multi-agent environments through on-demand collision avoidance and event-triggered replanning.

Contribution

The paper presents a novel distributed MPC approach with on-demand collision avoidance and event-triggered replanning, enhancing multi-robot trajectory generation in real-time.

Findings

Reduces average travel time by 50% compared to BVC approach.

Achieves over 90% success rate with 30 quadrotors in confined space.

Successfully validated with a swarm of 20 drones in experiments.

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.