Multi-robot Dubins Coverage with Autonomous Surface Vehicles
This addresses the challenge of large-scale environmental monitoring with non-holonomic vehicles, offering a practical solution for marine exploration, though it is incremental as it builds on existing multi-robot and Dubins coverage frameworks.
The paper tackles the problem of complete coverage of a known environment using a multi-robot team of Dubins vehicles, such as Autonomous Surface Vehicles for aquatic monitoring, by proposing heuristic methods based on k-TSP and clustering, achieving efficient solutions tested in simulations and real-world lake operations.
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.