Faculty of Engineering and Information Technologies, School of Aerospace, Mechanical and Mechatronic Engineering
Abstract
This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations
