Information-Theoretic Motion Planning for Constrained Sensor Networks

This paper considers the problem of online informative motion planning for a network of heterogeneous mobile sensing agents, each subject to dynamic constraints, environmental constraints, and sensor limitations. Previous work has not yielded algorithms that are amenable to such general constraint characterizations. In this paper, the information-rich rapidly-exploring random tree algorithm is proposed as a solution to the constrained informative motion planning problem that embeds metrics on uncertainty reduction at both the tree growth and path selection levels. The proposed algorithm possesses a number of beneficial properties, chief among them being the ability to quickly find dynamically feasible, informative paths, even subject to the aforementioned constraints. The utility of the proposed algorithm in efficiently localizing stationary targets is demonstrated in a progression of simulation results with both single-agent and multiagent networks. These results show that the information-rich rapidly-exploring random tree algorithm can be used online and in real-time to generate and execute information-rich paths in tightly constrained environments.