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

2026-07-12 · Source: Artificial Intelligence · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Coverage path planning (CPP) is a core problem in robot motion planning, focused on generating robot trajectories for complete workspace coverage while minimizing objectives like path length, overlap, turns, and energy. This field has broad applications in cleaning, inspection, agriculture, and surveillance. A comprehensive survey reviews 125 representative works published primarily between 2015 and 2026, tracing recent developments from classical CPP methods predating 2015. The survey categorizes CPP into six main areas: single-robot, multi-robot, 3D, constrained, learning-based, and visual CPP. For each, it outlines planning formulations, algorithms, strengths, and limitations, also analyzing how environmental knowledge, workspace geometry, robot constraints, sensing objectives, and coordination influence the problem. It also discusses open challenges in scalable online planning and multi-robot coordination.

Key takeaway

For Robotics Engineers developing autonomous systems, understanding the evolution and categorization of Coverage Path Planning (CPP) is crucial. You should evaluate current CPP methods across single-robot, multi-robot, 3D, and learning-based paradigms to select optimal strategies for specific applications like inspection or agriculture. Prioritize solutions addressing scalable online planning and multi-robot coordination to enhance system efficiency and robustness.

Key insights

Coverage Path Planning (CPP) evolves from classical single-robot methods to complex multi-robot, 3D, and learning-based systems.

Principles

• CPP minimizes path length and energy.
• Environmental factors shape CPP solutions.
• Multi-robot coordination is a key challenge.

Method

The survey organizes CPP methods into six categories: single-robot, multi-robot, 3D, constrained, learning-based, and visual CPP, summarizing formulations, algorithms, strengths, and limitations for each.

In practice

• Apply CPP for cleaning or inspection.
• Consider 3D CPP for complex environments.
• Explore learning-based methods for adaptation.

Topics

• Coverage Path Planning
• Robot Motion Planning
• Multi-robot Systems
• 3D Environments
• Learning-based Robotics
• Autonomous Systems

Best for: Robotics Engineer, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.