Structural Induced Exploration for Balanced and Scalable Multi-Robot Path Planning

TL;DR

This paper introduces a structure-induced exploration framework for multi-robot path planning that improves scalability, balance, and efficiency by integrating structural priors and load-aware objectives into ant colony optimization.

Contribution

It presents a novel framework combining structural priors with ACO to enhance multi-robot path planning scalability and workload balance, validated on diverse benchmarks.

Findings

Improved route compactness and stability.

Enhanced workload distribution among robots.

Scalable and interpretable planning framework.

Abstract

Multi-robot path planning is a fundamental yet challenging problem due to its combinatorial complexity and the need to balance global efficiency with fair task allocation among robots. Traditional swarm intelligence methods, although effective on small instances, often converge prematurely and struggle to scale to complex environments. In this work, we present a structure-induced exploration framework that integrates structural priors into the search process of the ant colony optimization (ACO). The approach leverages the spatial distribution of the task to induce a structural prior at initialization, thereby constraining the search space. The pheromone update rule is then designed to emphasize structurally meaningful connections and incorporates a load-aware objective to reconcile the total travel distance with individual robot workload. An explicit overlap suppression strategy further ensures that tasks remain distinct and balanced across the team. The proposed framework was validated on diverse benchmark scenarios covering a wide range of instance sizes and robot team configurations. The results demonstrate consistent improvements in route compactness, stability, and workload distribution compared to representative metaheuristic baselines. Beyond performance gains, the method also provides a scalable and interpretable framework that can be readily applied to logistics, surveillance, and search-and-rescue applications where reliable large-scale coordination is essential.