Multi-Robot Trajectory Planning with Feasibility Guarantee and Deadlock Resolution: An Obstacle-Dense Environment

TL;DR

This paper introduces a multi-robot trajectory planning approach that guarantees feasibility and deadlock resolution in obstacle-dense environments through recursive optimization, safe corridor generation, and a dynamic-priority deadlock handling mechanism, validated by simulations and hardware tests.

Contribution

The paper proposes a novel recursive optimization framework with online safe corridor generation and a dynamic-priority deadlock resolution method for multi-robot planning in cluttered spaces.

Findings

Improved safety and success rate over existing methods.

Validated with hardware experiments involving up to eight nano-quadrotors.

Effective deadlock resolution in complex obstacle environments.

Abstract

This article presents a multi-robot trajectory planning method which not only guarantees optimization feasibility and but also resolves deadlocks in obstacle-dense environments. The method is proposed via formulating a recursive optimization problem, where a novel safe corridor is generated online to ensure obstacle avoidance in trajectory planning. A dynamic-priority mechanism is combined with the right-hand rule to handle potential deadlocks that are much harder to resolve due to static obstacles. Comparisons with other state-of-the-art results are conducted to validate the improved safety and success rate. Additional hardware experiments are carried out with up to eight nano-quadrotors in various cluttered scenarios.