Priority-Aware Multi-Robot Coverage Path Planning

2026-01-02

IEEE Robotics and Automation Letters

AI Summary

This work addresses the limitation of traditional multi-robot coverage path planning (MCPP), which typically assumes uniform region importance and thus fails to prioritize critical areas in real-world applications. The paper presents the first formal definition of a priority-aware MCPP problem and introduces a two-stage solution framework that lexicographically optimizes weighted latency for high-priority regions followed by total mission completion time. The proposed approach integrates greedy region allocation, local search refinement, spanning tree-based path planning, and a Steiner tree-guided residual coverage strategy. Experimental results across diverse scenarios demonstrate that the method significantly reduces weighted latency for high-priority regions while maintaining total completion times comparable to baseline methods, and exhibits strong scalability with increasing numbers of robots and regions.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi-Robot Coverage Path Planning (MCPP), the objective is typically to minimize the makespan by generating non-overlapping paths for full-area coverage. However, most existing methods assume uniform importance across regions, limiting their effectiveness in scenarios where some zones require faster attention. We introduce the Priority-Aware MCPP (PA-MCPP) problem, where a subset of the environment is designated as prioritized zones with associated weights. The goal is to minimize, in lexicographic order, the total priority-weighted latency of zone coverage and the overall makespan. To address this, we propose a scalable two-phase framework combining (1) greedy zone assignment with local search, spanning-tree-based path planning, and (2) Steiner-tree-guided residual coverage. Experiments across diverse scenarios demonstrate that our method significantly reduces priority-weighted latency compared to standard MCPP baselines, while maintaining competitive makespan. Sensitivity analyses further show that the method scales well with the number of robots and that zone coverage behavior can be effectively controlled by adjusting priority weights.

Problem

• Multi-Robot Coverage Path Planning
• Priority-Aware
• Latency
• Makespan
• Coverage Tasks

Innovation

• Priority-Aware Coverage
• Multi-Robot Path Planning
• Weighted Latency Optimization
• Steiner-Tree Guidance
• Lexicographic Objective