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

2025-12-25

AI Summary

Multi-robot path planning faces dual challenges: balancing global efficiency with task fairness, and overcoming the premature convergence and poor scalability of conventional swarm intelligence algorithms. This paper proposes a structure-guided exploration framework that models spatial task distribution as a structural prior to constrain the search space. We introduce a load-aware pheromone update mechanism and an explicit overlap suppression strategy to jointly optimize path compactness, operational stability, and workload balance. Built upon enhanced ant colony optimization, our method integrates structural-prior initialization, dynamic pheromone updating, task-deduplication constraints, and scalable graph search. Evaluated on multi-scale benchmarks, it achieves a 23% improvement in path compactness, a 37% reduction in runtime variance, and a 41% decrease in workload standard deviation, enabling real-time coordination for over one hundred robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Balances global efficiency with fair task allocation in multi-robot path planning
• Scales multi-robot planning to complex environments using structural priors
• Improves route compactness, stability, and workload distribution in coordination

Innovation

• Integrates structural priors into ant colony optimization
• Uses load-aware pheromone updates for balanced workload
• Applies overlap suppression for distinct task allocation