# Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

arXiv: https://arxiv.org/abs/2006.16460 · HTML: https://mlsanity.com/paper/2006.16460/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Centroid Distance Keypoint Detector for Colored Point Clouds](https://mlsanity.com/paper/2210.01298/) (2022)
- [Online Concurrent Multi-Robot Coverage Path Planning](https://mlsanity.com/paper/2403.10460/) (2024)
- [DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space](https://mlsanity.com/paper/2603.02976/) (2026)
- [Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments](https://mlsanity.com/paper/2402.06297/) (2024)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

Online coverage planning can be useful in applications like field monitoring and search and rescue. Without prior information of the environment, achieving resolution-complete coverage considering the non-holonomic mobility constraints in commonly-used vehicles (e.g., wheeled robots) remains a challenge. In this paper, we propose a hierarchical, hex-decomposition-based coverage planning algorithm for unknown, obstacle-cluttered environments. The proposed approach ensures resolution-complete coverage, can be tuned to achieve fast exploration, and plans smooth paths for Dubins vehicles to follow at constant velocity in real-time. Gazebo simulations and hardware experiments with a non-holonomic wheeled robot show that our approach can successfully tradeoff between coverage and exploration speed and can outperform existing online coverage algorithms in terms of total covered area or exploration speed according to how it is tuned.