Distributed Adaptive Environmental Sampling with AUVs: Cooperation and Team Coordination through Minimum-Spanning-Tree Graph Searching Algorithms

Abstract

A distributed adaptive sampling algorithm suitable for a team of Autonomous Underwater Vehicles exploring an oceanic region is proposed. The algorithm fulfils the following goals: the sampling stations are incrementally chosen in order to satisfy the accuracy requirements on the measured quantity, on the basis of the local smoothness of the measured field as estimated from the data; the vehicles in the team are spread over the region in order to maximize area coverage; the vehicles are constrained to maintain a maximum range from each other, in order to preserve a communication link among the team. To fulfil the goal, a multi-objective optimization problem is iteratively solved in a distributed fashion by the vehicles in the team through application of minimum-spanning-tree graph search algorithms. The simulation results reported show how the application of this approach determines a behaviour of the team in which the vehicles travel in formation, eventually reconfiguring the formation as it may become necessary in the course of the mission.