Active Information Gathering Using Distributed Mobile Sensing Networks

Abstract

An autonomous robot system requires robots to actively gather information using sensors in order to make control decisions. Some problems where autonomous robots are useful include mapping, environmental monitoring, and surveillance. In some cases, information gathering turns into a multiple target tracking (MTT) problem. Usually, an MTT tracker is utilized to recursively estimate both the number of targets and the state of each target. In order to estimate more efficiently and reliably, sensors must balance exploiting current knowledge to track known targets while simultaneously exploring to find information about new targets. This yields to the coverage control problem, which is aimed at maximizing the total sensing capability of a sensing network over the entire mission space. Many applications of sensing networks benefit from utilizing distributed manners, in which cases networks are able to be scaled to large swarms and better tolerate failures of individual sensors. A distributed network requires sensors to exchange data locally and cooperate in decision making globally.This dissertation studies MTT based on random finite set (RFS) for iterative target states estimation and Voronoi-based coverage control algorithms for target tracking. We address a series of four main problems aiming at allowing reliable and efficient target tracking for distributed multi-robot systems in complicated real-world scenarios and push forward the realization of robot coordination techniques. Firstly, we propose novel target estimation and coverage control schemes to incorporate robots with localization uncertainty. Secondly, we improve target search efficiency for teams of robot with no prior knowledge of target models or distributions by enabling active search and environment learning. Thirdly, we allow robots with heterogeneous capacities in perception and kinematics to cooperatively search and track in an efficient way. Lastly, we develop an improved MTT tracker to allow estimating semantic object labels over time. The efficacy of the proposed methods has been validated in series of simulations and/or hardware validations.