Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees

TL;DR

This paper introduces a distributed, rule-based Lloyd algorithm (RBL) for multi-robot motion planning that guarantees safety and convergence without communication, effectively handling dynamic constraints and enhancing learning-based approaches.

Contribution

The paper develops RBL, a novel distributed algorithm that ensures safety and convergence in multi-robot systems without communication, and demonstrates its effectiveness across various robot types.

Findings

RBL guarantees safety and convergence without robot communication.

The algorithm handles dynamic constraints with control input saturation.

Experimental validation confirms scalability and reliability.

Abstract

This paper presents a distributed rule-based Lloyd algorithm (RBL) for multi-robot motion planning and control. The main limitations of the basic Loyd-based algorithm (LB) concern deadlock issues and the failure to address dynamic constraints effectively. Our contribution is twofold. First, we show how RBL is able to provide safety and convergence to the goal region without relying on communication between robots, nor synchronization between the robots. We considered different dynamic constraints with control inputs saturation. Second, we show that the Lloyd-based algorithm (without rules) can be successfully used as a safety layer for learning-based approaches, leading to non-negligible benefits. We further prove the soundness, reliability, and scalability of RBL through extensive simulations, comparisons with the state of the art, and experimental validations on small-scale car-like robots, unicycle-like robots, omnidirectional robots, and aerial robots on the field.