CAP: A Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments using Coverage Guidance Graph

2025-03-01

AI Summary

To address the challenge of balancing global efficiency and local responsiveness in coverage path planning (CPP) under unknown environments, this paper proposes a hierarchical online path planning framework. The core contribution is the first introduction of a Coverage Guidance Graph (CGG), which explicitly models environmental topology and connectivity constraints; the CGG is incrementally constructed and dynamically updated via real-time sensor fusion. At the high level, the framework generates a connectivity-aware global coverage sequence; at the low level, it optimizes collision-free local paths. Extensive evaluations in simulation and on physical robots demonstrate that the method significantly outperforms five state-of-the-art baseline algorithms across three key metrics—coverage time, path length, and overlap ratio—validating its efficacy and robustness for efficient full-coverage navigation in complex, unknown environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Planning under Uncertainty
• 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: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Efficient coverage of unknown environments requires robots to adapt their paths in real time based on on-board sensor data. In this paper, we introduce CAP, a connectivity-aware hierarchical coverage path planning algorithm for efficient coverage of unknown environments. During online operation, CAP incrementally constructs a coverage guidance graph to capture essential information about the environment. Based on the updated graph, the hierarchical planner determines an efficient path to maximize global coverage efficiency and minimize local coverage time. The performance of CAP is evaluated and compared with five baseline algorithms through high-fidelity simulations as well as robot experiments. Our results show that CAP yields significant improvements in coverage time, path length, and path overlap ratio.

Problem

• Develops CAP for real-time path planning in unknown environments.
• Uses coverage guidance graph to enhance global and local efficiency.
• Compares CAP with baseline algorithms to validate performance improvements.

Innovation

• Hierarchical path planning for unknown environments
• Real-time coverage guidance graph construction
• Efficiency in global coverage and local time