Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments

2025-09-18

AI Summary

To address path redundancy and frequent collisions in multi-robot collaborative coverage path planning within unknown large-scale environments, this paper proposes Multi-CAP, a hierarchical algorithm. Methodologically, it (1) constructs a connectivity-aware dynamic adjacency graph to incrementally model environmental topology online; (2) formulates the coverage task as a Vehicle Routing Problem (VRP) for conflict-free global subregion assignment; and (3) enables each robot to adaptively optimize its local coverage path using onboard sensor data. The approach supports fully distributed execution and online exploration. Extensive simulations and real-world experiments demonstrate that Multi-CAP significantly outperforms state-of-the-art methods in coverage time, total path length, and path overlap ratio. Ablation studies confirm the critical contributions of both the connectivity-aware mapping and the VRP-based hierarchical planning.

Technology Category

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

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: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Efficient coordination of multiple robots for coverage of large, unknown environments is a significant challenge that involves minimizing the total coverage path length while reducing inter-robot conflicts. In this paper, we introduce a Multi-robot Connectivity-Aware Planner (Multi-CAP), a hierarchical coverage path planning algorithm that facilitates multi-robot coordination through a novel connectivity-aware approach. The algorithm constructs and dynamically maintains an adjacency graph that represents the environment as a set of connected subareas. Critically, we make the assumption that the environment, while unknown, is bounded. This allows for incremental refinement of the adjacency graph online to ensure its structure represents the physical layout of the space, both in observed and unobserved areas of the map as robots explore the environment. We frame the task of assigning subareas to robots as a Vehicle Routing Problem (VRP), a well-studied problem for finding optimal routes for a fleet of vehicles. This is used to compute disjoint tours that minimize redundant travel, assigning each robot a unique, non-conflicting set of subareas. Each robot then executes its assigned tour, independently adapting its coverage strategy within each subarea to minimize path length based on real-time sensor observations of the subarea. We demonstrate through simulations and multi-robot hardware experiments that Multi-CAP significantly outperforms state-of-the-art methods in key metrics, including coverage time, total path length, and path overlap ratio. Ablation studies further validate the critical role of our connectivity-aware graph and the global tour planner in achieving these performance gains.

Problem

• Minimizing coverage path length for multi-robot coordination
• Reducing inter-robot conflicts in unknown bounded environments
• Maintaining connectivity awareness through dynamic adjacency graphs

Innovation

• Hierarchical coverage path planning algorithm
• Connectivity-aware adjacency graph maintenance
• Vehicle Routing Problem for disjoint tours