Communication Planning for Cooperative Terrain-Based Underwater Localization

This paper presents a decentralized communication planning algorithm for cooperative terrain-based navigation (dec-TBN) with autonomous underwater vehicles. The proposed algorithm uses forward simulation to approximate the value of communicating at each time step. The simulations are used to build a directed acyclic graph that can be searched to provide a minimum cost communication schedule. Simulations and field trials are used to validate the algorithm. The simulations use a real-world bathymetry map from Lake Nighthorse, CO, and a sensor model derived from an Ocean Server Iver2 vehicle. The simulation results show that the algorithm finds a communication schedule that reduces communication bandwidth by 86% and improves robot localization by up to 27% compared to non-cooperative terrain-based navigation. Field trials were conducted in Foster Reservoir, OR, using two Riptide Autonomous Solutions micro-unmanned underwater vehicles. The vehicles collected GPS, altimeter, acoustic communications, and dead reckoning data while following paths on the surface of the reservoir. The data were used to evaluate the planning algorithm. In three of four missions, the planning algorithm improved dec-TBN localization while reducing acoustic communication bandwidth by 56%. In the fourth mission, dec-TBN performed better when using full communications bandwidth, but the communication policy for that mission maintained 86% of the localization accuracy while using 9% of the communications. These results indicate that the presented communication planning algorithm can maintain or improve dec-TBN accuracy while reducing the number of communications used for localization.