Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions
2026-07-12 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors review how robots can plan paths to cover areas completely while using the least resources like time, energy, or turns. They look at 125 papers from 2015 to 2026 and organize recent advances into six groups including single-robot and multi-robot planning, 3D environments, and learning-based methods. The review explains different approaches, their pros and cons, and how factors like the shape of the workspace or robot limits affect planning. They also point out challenges such as coordinating many robots and doing this planning in real-time. Overall, the authors give a clear summary of the current state and future directions in robot coverage path planning.
Coverage Path PlanningRobot Motion PlanningMulti-Robot Systems3D EnvironmentsLearning-Based PlanningVisual CoverageWorkspace GeometryRobot ConstraintsOnline PlanningMulti-Robot Coordination
Authors
Zongyuan Shen, Shalabh Gupta, Shancheng Zhao, Dehua Zhou, Gao Wang, Zhongqiang Ren, Yaming Ou, Yikui Zhai, C. L. Philip Chen
Abstract
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.