A new approach to multi-robot harbour patrolling: Theory and experiments

This paper describes a decentralized coordination strategy for multi robot border patrolling missions. In the last decade, much effort has been given to the sampling of dynamic fields by means of Gaussian Processes. The problem is the estimation of a partially or totally unknown scalar field where a Gaussian distribution is assumed. The aim of the proposed paper is to properly adapt and apply this technique to the problem of multi-robot harbor patrolling. The key point is the introduction of a time varying dependency in the probabilistic formulation, thus allowing for the sampled field to be dynamic (i.e., changing in time). This makes the proposed solution suitable for patrolling missions. Given the nature of the problem, the focus is on vehicle motion generation and coordination. In particular, the strategy lends itself to implementation on multiple, autonomous, decentralized robots. The advantages of Voronoi tessellations are exploited to automatically distribute the vehicles over the environment. The resulting algorithm takes into account several constraints and can be tailored based on the communication and computational capabilities of the robots, thus making it suitable for heterogeneous systems. Numerical simulations illustrate the applicability of the theory developed. The results of experiments done with three autonomous marine surface vehicles in a harbour scenario at the Parque Expo site in Lisbon are reported and discussed.