Communication-aware information gathering with dynamic information flow

Abstract

© The Author(s) 2014. We are interested in the problem of how to improve estimation in multi-robot information gathering systems by actively controlling the rate of communication between robots. Communication is essential in such systems for decentralized data fusion and decision-making, but wireless networks impose capacity constraints that are frequently overlooked. In order to make efficient use of available capacity, it is necessary to consider a fundamental trade-off between communication cost, computation cost and information value. We introduce a new problem, dynamic information flow, that formalizes this trade-off in terms of decentralized constrained optimization. We propose algorithms that dynamically adjust the data rate of each communication link to maximize an information gain metric subject to constraints on communication and computation resources. The metric is balanced against the communication resources required to transmit data and the computation cost of processing sensor data to form observations. The optimization process selectively routes raw sensor data or processed observation data to zero, one or many robots. Our algorithms therefore allow large systems with many different types of sensors and computational resources to maximize information gain performance while satisfying realistic communication constraints. We also present experimental results with multiple ground robots and multiple sensor types that demonstrate the benefit of dynamic information flow in comparison to simpler bandwidth-limiting methods