A Generalized Voronoi Graph based Coverage Control Approach for Non-Convex Environment

2026-03-10

AI Summary

This work addresses the challenge of achieving efficient coverage control for multi-robot systems in non-convex environments with obstacles. The authors propose a two-stage cooperative coverage approach based on the Generalized Voronoi Diagram (GVG). In the first stage, a weighted load-balancing mechanism that accounts for heterogeneity in subregion quality is introduced to optimally partition the non-convex domain and allocate robots accordingly. In the second stage, a novel distributed controller is designed to drive each robot to cooperatively complete coverage within its assigned subregion. Theoretical analysis establishes the convergence of the proposed algorithm, and simulation results demonstrate that the method significantly improves both coverage efficiency and load-balancing performance in non-convex, obstacle-cluttered environments.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

To address the challenge of efficient coverage by multi-robot systems in non-convex regions with multiple obstacles, this paper proposes a coverage control method based on the Generalized Voronoi Graph (GVG), which has two phases: Load-Balancing Algorithm phase and Collaborative Coverage phase. In Load-Balancing Algorithm phase, the non-convex region is partitioned into multiple sub-regions based on GVG. Besides, a weighted load-balancing algorithm is developed, which considers the quality differences among sub-regions. By iteratively optimizing the robot allocation ratio, the number of robots in each sub-region is matched with the sub-region quality to achieve load balance. In Collaborative Coverage phase, each robot is controlled by a new controller to effectively coverage the region. The convergence of the method is proved and its performance is evaluated through simulations.

Problem

• coverage control
• multi-robot systems
• non-convex environment
• obstacle avoidance
• region partitioning

Innovation

• Generalized Voronoi Graph
• Coverage Control
• Load-Balancing Algorithm
• Non-Convex Environment
• Multi-Robot Systems