# Multi-robot Dubins Coverage with Autonomous Surface Vehicles

arXiv: https://arxiv.org/abs/1808.02552 · HTML: https://mlsanity.com/paper/1808.02552/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

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

- [Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation](https://mlsanity.com/paper/2112.10593/) (2021)
- [MarineDet: Towards Open-Marine Object Detection](https://mlsanity.com/paper/2310.01931/) (2023)
- [Online Concurrent Multi-Robot Coverage Path Planning](https://mlsanity.com/paper/2403.10460/) (2024)
- [Dynamic Autonomous Surface Vehicle Control and Applications in Environmental Monitoring](https://mlsanity.com/paper/2103.15951/) (2021)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal, as they may take too long to cover a large area. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. This paper focuses on environmental monitoring of aquatic environments using Autonomous Surface Vehicles (ASVs). In particular, we propose a novel approach for solving the problem of complete coverage of a known environment by a multi-robot team consisting of Dubins vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NP-complete problems. As such, we present two heuristics methods based on a variant of the traveling salesman problem -- k-TSP -- formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations to assess their scalability and with a team of ASVs operating on a lake to ensure their applicability in real world.