发表机构
College of Information Science and Technology, Jinan University; Department of Electrical and Computer Engineering, University of Connecticut; Global College, Shanghai Jiao Tong University; School of Artificial Intelligence, University of Chinese Academy of Sciences; School of Electronics and Information Engineering, Wuyi University; School of Computer Science and Engineering, South China University of Technology（暨南大学信息科学技术学院; 康涅狄格大学电气与计算机工程系; 上海交通大学密西根学院; 中国科学院大学人工智能学院; 五邑大学电子与信息工程学院; 华南理工大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文全面综述2015至2026年125篇覆盖路径规划（CPP）代表性作品，结合经典方法展示其演变。CPP方法分六类，总结各类规划公式、算法等，分析相关因素对问题的塑造，讨论开放挑战，提供了发展概况与未来研究方向。
AI 中文摘要
覆盖路径规划（CPP）是机器人运动规划中的一个基本问题，旨在生成能完全覆盖目标工作区的机器人轨迹，同时最小化路径长度、重叠、转弯次数和能耗等特定任务目标。它在清洁、检查、测绘、农业、制造、监视、排雷和环境监测等领域有广泛应用。本文对2015年至2026年期间发表的125篇代表性作品进行了全面综述，结合2015年前的经典CPP方法展示了近期发展的演变。CPP方法分为单机器人CPP、多机器人CPP、3D CPP、受限CPP、基于学习的CPP和视觉CPP六类，对每类总结了主要规划公式、代表性算法、优缺点等。此外，分析了环境知识等如何塑造CPP问题，并讨论了可扩展在线规划等方面的开放挑战，提供了近期CPP发展和未来研究方向的结构化概述。
英文摘要
Coverage path planning (CPP) is a fundamental problem in robot motion planning, whose aim is to produce robot trajectories that provide complete coverage of target workspaces while minimizing task-specific objectives such as path length, overlap, number of turns, and energy consumption. CPP has widespread applications in cleaning, inspection, mapping, agriculture, manufacturing, surveillance, demining, and environmental monitoring. Although classical CPP has been extensively studied, recent advances have extended CPP beyond single-robot settings to multi-robot systems, complex 3D environments, constrained platforms, learning-based coverage planning, and visual coverage tasks. This paper presents a comprehensive survey of 125 representative works published primarily between 2015 and 2026, while presenting the evolution of recent developments in light of the classical CPP methods published before 2015. The CPP methods are organized into six main categories: single-robot CPP, multi-robot CPP, 3D CPP, constrained CPP, learning-based CPP, and visual CPP. For each category, the review summarizes the main planning formulations, representative algorithms, strengths, and limitations. In addition, the review analyzes how environmental knowledge, workspace geometry, robot constraints, sensing objectives, and coordination requirements shape the CPP problem. The survey further discusses open challenges in scalable online planning, multi-robot coordination, 3D and visual coverage, unified platform-constrained and resource-aware coverage, and learning-enhanced coverage. Thus, the survey provides a structured overview of recent CPP developments and future research directions.
