A Hybrid Evolutionary Approach for Multi Robot Coordinated Planning at Intersections

TL;DR

This paper introduces a hybrid evolutionary algorithm combining lattice-based configuration and RRT to improve multi-robot intersection planning, demonstrating superior performance over existing methods in complex scenarios.

Contribution

A novel hybrid evolutionary approach that enhances multi-robot intersection planning by integrating parametric lattice configurations with discrete RRT methods.

Findings

Proposed algorithm outperforms seven related approaches in complex scenarios.

New sampling and representation mechanisms improve optimization-based multi-robot navigation.

Algorithm demonstrates feasibility and superiority in computational experiments.

Abstract

Coordinated multi-robot motion planning at intersections is key for safe mobility in roads, factories and warehouses. The rapidly exploring random tree (RRT) algorithms are popular in multi-robot motion planning. However, generating the graph configuration space and searching in the composite tensor configuration space is computationally expensive for large number of sample points. In this paper, we propose a new evolutionary-based algorithm using a parametric lattice-based configuration and the discrete-based RRT for collision-free multi-robot planning at intersections. Our computational experiments using complex planning intersection scenarios have shown the feasibility and the superiority of the proposed algorithm compared to seven other related approaches. Our results offer new sampling and representation mechanisms to render optimization-based approaches for multi-robot navigation.