Belief space planning for underwater cooperative localization

Summary

Jeffrey M. Walls, Stephen M. Chaves, Enric Galceran and Ryan M. Eustice, Belief space planning for underwater cooperative localization. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2264-2271, Hamburg, Germany, September 2015.

Abstract

This paper reports on the inclusion of a probabilistic channel model within a cooperative localization planning framework. Underwater cooperative localization reduces positioning errors by sharing sensor data across a team of underwater vehicles. Relative range constraints between vehicles are measured by the one-way-travel-time of successfully received acoustic communication broadcasts. The quality of the navigation solution is intimately linked to the geometry of the network and, therefore, can benefit from planning informative relative trajectories. We cast this planning problem as an instance of belief space planning. In order to weight packet loss over the acoustic channel, we introduce a probabilistic channel model into the planning framework. We propose an optimization algorithm that allows us to plan open-loop control actions and, by extension, closed-loop parameterized trajectories.

Bibtex entry