Information-rich Path Planning with General Constraints using Rapidly-exploring Random Trees

Abstract

This paper introduces the Information-rich Rapidly-exploring Random Tree (IRRT), an extension of the RRT algorithm that embeds information collection as predicted using Fisher Information Matrices. The primary contribution of this algorithm is target-based information maximization in general (possibly heavily constrained) environments, with complex vehicle dynamic constraints and sensor limitations, including limited resolution and narrow eld-of-view. An extension of IRRT for multi-agent missions is also presented. IRRT is distinguished from previous solutions strategies by its computational tractability and general constraint characterization. A progression of simulation results demonstrates that this implementation can generate complex target-tracking behaviors from a simple model of the trade-o between information gathering and goal arrival.