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

arXiv: https://arxiv.org/abs/2607.10649 · HTML: https://mlsanity.com/paper/2607.10649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 43 of its 154 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 30 of 43)

- [Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks](https://mlsanity.com/paper/2011.01119/) (2020)
- [UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02609/) (2020)
- [MSTC*:Multi-robot Coverage Path Planning under Physical Constraints](https://mlsanity.com/paper/2108.04632/) (2021)
- [Efficient Multi-Robot Coverage of a Known Environment](https://mlsanity.com/paper/1808.02541/) (2018)
- [Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures](https://mlsanity.com/paper/2007.13065/) (2020)
- [Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas](https://mlsanity.com/paper/2103.14709/) (2021)
- [FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes](https://mlsanity.com/paper/2309.13882/) (2023)
- [Multi-robot Dubins Coverage with Autonomous Surface Vehicles](https://mlsanity.com/paper/1808.02552/) (2018)
- [Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments](https://mlsanity.com/paper/2006.16460/) (2020)
- [PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting](https://mlsanity.com/paper/2211.06930/) (2022)
- [Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction](https://mlsanity.com/paper/2411.01707/) (2024)
- [CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failures](https://mlsanity.com/paper/1905.12191/) (2019)
- [ε^*+: An Online Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehicles](https://mlsanity.com/paper/2008.13041/) (2020)
- [CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees](https://mlsanity.com/paper/2010.09231/) (2020)
- [NEPTUNE: Nonentangling Trajectory Planning for Multiple Tethered Unmanned Vehicles](https://mlsanity.com/paper/2212.01536/) (2022)
- [SMART: Self-Morphing Adaptive Replanning Tree](https://mlsanity.com/paper/2305.06487/) (2023)
- [Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics](https://mlsanity.com/paper/2306.17609/) (2023)
- [Robot Coverage Path Planning for General Surfaces Using Quadratic Differentials](https://mlsanity.com/paper/1701.07549/) (2017)
- [Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection](https://mlsanity.com/paper/1908.02901/) (2019)
- [Coverage Path Planning for Robotic Quality Inspection with Control on Measurement Uncertainty](https://mlsanity.com/paper/2201.04310/) (2022)
- [Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks](https://mlsanity.com/paper/2208.02993/) (2022)
- [Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces](https://mlsanity.com/paper/2303.03616/) (2023)
- [AG-CVG: Coverage Planning with a Mobile Recharging UGV and an Energy-Constrained UAV](https://mlsanity.com/paper/2310.07621/) (2023)
- [Generalized Multi-Speed Dubins Motion Model](https://mlsanity.com/paper/2402.01122/) (2024)
- [Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight](https://mlsanity.com/paper/2402.10529/) (2024)
- [Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure](https://mlsanity.com/paper/2403.00613/) (2024)
- [C*: A Coverage Path Planning Algorithm for Unknown Environments using Rapidly Covering Graphs](https://mlsanity.com/paper/2505.13782/) (2025)
- [Coverage Path Planning with Track Spacing Adaptation for Autonomous Underwater Vehicles](https://mlsanity.com/paper/2006.12896/) (2020)
- [Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning](https://mlsanity.com/paper/2109.08185/) (2021)
- [Online Coverage Planning for an Autonomous Weed Mowing Robot with Curvature Constraints](https://mlsanity.com/paper/2111.10462/) (2021)

## Abstract (arXiv)

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.