TRACE：使用分层覆盖树实现未知环境的覆盖路径规划

中文摘要 AI 辅助

本文提出了一种新颖的在线覆盖路径规划（CPP）算法，称为TRACE，用于未知环境的实时覆盖。TRACE基于一个分层覆盖树，该树提供了未覆盖空间演化连通性的全局表示。随着环境被逐步揭示和覆盖，新发现的障碍物和已覆盖单元可能将剩余的未覆盖空间分割成不连通的区域。TRACE递归地扩展相应的树节点，以显式表示这些区域并将其组织起来用于后续的覆盖规划。基于更新后的树，维护一条增量全局路径以引导覆盖过程。TRACE仅局部优化受影响的区域，同时保持未变化区域的访问顺序，从而减少全局重新规划的计算负担，并保持一致的覆盖进展。在全局路径的引导下，局部规划器生成来回覆盖路径，并切换到全局路径感知规划以高效完成目标区域。理论分析确立了TRACE的计算复杂度和完全覆盖性质，并推导了增量全局路径优化的近似界。通过使用移动机器人的广泛高保真仿真和真实机器人实验评估了TRACE的性能。与六种现有CPP方法的比较评估表明，TRACE在覆盖时间、路径长度、重叠率和转弯次数方面均有显著改进。

英文摘要

This paper presents a novel online coverage path planning (CPP) algorithm, called TRACE, for real-time coverage of unknown environments. TRACE is built upon a hierarchical coverage tree that provides a global representation of the evolving connectivity of the uncovered space. As the environment is incrementally revealed and covered, newly discovered obstacles and covered cells may fragment the remaining uncovered space into disconnected regions. TRACE recursively expands the corresponding tree nodes to explicitly represent these regions and organize them for subsequent coverage planning. Based on the updated tree, an incremental global tour is maintained to guide the coverage process. TRACE locally refines only the affected portions while preserving the visiting order of unchanged regions, thereby reducing the computational burden of global replanning and maintaining a consistent coverage progression. Guided by the global tour, a local planner generates back-and-forth coverage paths and switches to global-tour-aware planning to efficiently complete the target regions. Theoretical analysis establishes the computational complexity and complete coverage property of TRACE, and derives an approximation bound for the incremental global tour refinement. The performance of TRACE is evaluated through extensive high-fidelity simulations and real-robot experiments using a mobile robot. Comparative evaluations against six existing CPP methods demonstrate significant improvements in coverage time, path length, overlap ratio, and number of turns.

发表机构

• Jinan University（暨南大学）
• University of Chinese Academy of Sciences（中国科学院大学）
• Shanghai Jiao Tong University（上海交通大学）
• Wuyi University（五邑大学）
• South China University of Technology（华南理工大学）

机构由 AI 辅助整理，请以论文原文为准。