Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions

2026-07-12

AI Summary

This study presents a systematic review of 125 representative coverage path planning (CPP) works from 2015 to 2026, addressing the multi-objective optimization challenges inherent in robotic coverage tasks—such as minimizing path length, overlap, turning frequency, and energy consumption. It introduces, for the first time, a structured taxonomy encompassing six categories: single-robot, multi-robot, 3D environments, constrained platforms, learning-driven approaches, and visual coverage. By analyzing differences in modeling strategies, algorithmic evolution, and application contexts across these categories, the work elucidates how environmental priors, geometric features, platform constraints, and perceptual objectives critically shape problem formulation. The review synthesizes the strengths and limitations of existing techniques, identifies core open challenges, and outlines promising future directions, including scalable online planning, multi-robot coordination, and integration of 3D and visual coverage paradigms, thereby offering a comprehensive theoretical foundation and roadmap for CPP research.

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.

Problem

• Coverage Path Planning
• Robot Motion Planning
• Multi-robot Systems
• 3D Environments
• Visual Coverage

Innovation

• multi-robot coverage
• 3D coverage path planning
• learning-based CPP
• visual coverage
• constrained platforms