Autonomous data collection from underwater sensor networks using acoustic communication

We examine the problem of planning paths for an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. This problem is closely related to the classical Traveling Salesperson Problem (TSP), but differs in that data from a particular sensor has a probability of being collected depending on the quality of communication. We propose methods for solving this problem by extending approximation algorithms for variants of TSP, and we compare our proposed algorithms to baseline strategies through simulated experiments with varying levels of communication quality. Our simulations utilize a realistic model of acoustic communication to determine the probability of acquiring data from each sensor. The results demonstrate that planning the tour for the entire network while exploiting the communication model during planning improves performance versus myopic methods.