Distributed Optimization of Nonlinear, Non-Gaussian, Communication-Aware Information using Particle Methods

This paper presents a distributed optimization framework and its local utility design for communication-aware information gathering by mobile robotic sensor networks. The main idea of the optimization is that each robot decides based on its local utility that considers the decisions of other neighbor robots higher in a given hierarchy. The local utility is designed as conditional mutual information that captures sensing and communication properties. Sampling procedures using a specific measurement set and particle methods are applied to compute the designed utility, which allows nonlinear, non-Gaussian properties of targets, sensing, and communication. Simulation results describe the presented distributed optimization shows more improved estimates and entropy reduction than another approach that does not consider communication properties. Simulation results also verify the presented distributed optimization using the described approach for information computation has better results than using other approaches that simplify the communication-aware information.