Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control

TL;DR

This paper presents a scalable distributed model predictive control algorithm for multiagent trajectory planning that efficiently avoids collisions and significantly reduces computation time, validated through simulations and quadrotor experiments.

Contribution

Introduces a novel distributed MPC-based trajectory generation method with an on-demand collision avoidance strategy for multiagent systems.

Findings

Reduces computation time by over 85% compared to previous methods.

Successfully validated with up to 25 quadrotors in indoor environments.

Maintains near-optimal trajectories despite reduced computational effort.

Abstract

This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control. Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbors, the agents are able to detect and avoid collisions while moving toward their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming, while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.