MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm

TL;DR

This paper introduces MC-Swarm, a distributed multi-agent trajectory planning algorithm for quadrotor swarms that operates asynchronously with minimal communication, guarantees deadlock resolution, and ensures collision avoidance in complex environments.

Contribution

It presents a novel asynchronous, communication-free trajectory planning method with deadlock resolution and collision avoidance guarantees for quadrotor swarms.

Findings

Effective deadlock resolution in complex environments

Collision avoidance guarantees under asynchronous updates

Reduced mission time with lightweight communication

Abstract

For effective multi-agent trajectory planning, it is important to consider lightweight communication and its potential asynchrony. This paper presents a distributed trajectory planning algorithm for a quadrotor swarm that operates asynchronously and requires no communication except during the initial planning phase. Moreover, our algorithm guarantees no deadlock under asynchronous updates and absence of communication during flight. To effectively ensure these points, we build two main modules: coordination state updater and trajectory optimizer. The coordination state updater computes waypoints for each agent toward its goal and performs subgoal optimization while considering deadlocks, as well as safety constraints with respect to neighbor agents and obstacles. Then, the trajectory optimizer generates a trajectory that ensures collision avoidance even with the asynchronous planning updates of neighboring agents. We provide a theoretical guarantee of collision avoidance with deadlock resolution and evaluate the effectiveness of our method in complex simulation environments, including random forests and narrow-gap mazes. Additionally, to reduce the total mission time, we design a faster coordination state update using lightweight communication. Lastly, our approach is validated through extensive simulations and real-world experiments with cluttered environment scenarios.