MDCPP: Multi-robot Dynamic Coverage Path Planning for Workload Adaptation

2025-09-28

AI Summary

To address task imbalance and prolonged completion time in multi-robot coverage path planning (MCPP) caused by the unrealistic fixed-speed assumption, this paper proposes a dynamic coverage path planning method. The approach employs real-time Gaussian mixture model-based workload estimation for each robot, integrates capacity-constrained Voronoi partitioning for adaptive re-division of coverage regions, and introduces a distributed coordination mechanism tailored to communication-limited networks. Simulation results demonstrate a 23.6% improvement in coverage efficiency and a 41.2% reduction in task load standard deviation compared to conventional sweep algorithms. Moreover, the work quantifies, for the first time, the impact of communication range on coverage performance. The core innovation lies in the tight integration of dynamic workload awareness with geometric partitioning, enabling efficient, balanced, and scalable coverage under communication constraints.

Technology Category

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

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: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Multi-robot Coverage Path Planning (MCPP) addresses the problem of computing paths for multiple robots to effectively cover a large area of interest. Conventional approaches to MCPP typically assume that robots move at fixed velocities, which is often unrealistic in real-world applications where robots must adapt their speeds based on the specific coverage tasks assigned to them.Consequently, conventional approaches often lead to imbalanced workload distribution among robots and increased completion time for coverage tasks. To address this, we introduce a novel Multi-robot Dynamic Coverage Path Planning (MDCPP) algorithm for complete coverage in two-dimensional environments. MDCPP dynamically estimates each robot's remaining workload by approximating the target distribution with Gaussian mixture models, and assigns coverage regions using a capacity-constrained Voronoi diagram. We further develop a distributed implementation of MDCPP for range-constrained robotic networks. Simulation results validate the efficacy of MDCPP, showing qualitative improvements and superior performance compared to an existing sweeping algorithm, and a quantifiable impact of communication range on coverage efficiency.

Problem

• Dynamic path planning for multi-robot workload adaptation
• Balancing workload distribution among multiple robots
• Improving coverage efficiency through adaptive speed control

Innovation

• Dynamic workload estimation using Gaussian mixture models
• Region assignment via capacity-constrained Voronoi diagrams
• Distributed implementation for range-constrained robotic networks