Multi-Robot Path Planning in Complex Environments via Graph Embedding

TL;DR

This paper introduces a novel graph embedding approach for multi-robot path planning in complex environments, ensuring feasibility and optimizing solutions through a combination of pebble graphs, parallel motions, and mesh optimization.

Contribution

It presents a new method combining pebble graph design, greedy optimization, and mesh embedding with differentiable constraints for effective multi-robot path planning in complex spaces.

Findings

Achieves 99.0% free-space coverage in complex environments

Improves robot density by 30.3%

Operates efficiently within hours on a desktop machine

Abstract

We propose an approach to solve multi-agent path planning (MPP) problems for complex environments. Our method first designs a special pebble graph with a set of feasibility constraints, under which MPP problems have feasibility guarantee. We further propose an algorithm to greedily improve the optimality of planned MPP solutions via parallel pebble motions. As a second step, we develop a mesh optimization algorithm to embed our pebble graph into arbitrarily complex environments. We show that the feasibility constraints of a pebble graph can be converted into differentiable geometric constraints, such that our mesh optimizer can satisfy these constraints via constrained numerical optimization. We have evaluated the effectiveness and efficiency of our method using a set of environments with complex geometries, on which our method achieves an average of 99.0% free-space coverage and 30.3% robot density within hours of computation on a desktop machine.