Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring

In this article, we propose a sampling-based motion planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a maximization problem with a budget constraint. Our approach is built on rapidly-exploring information gathering algorithms and beneVts from advantages of sampling-based optimal motion planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for the entire exploration and information gathering mission based on the least upper bound of the average map entropy. The convergence criterion gives rise to a natural automatic stopping criterion for information-driven motion control. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor conVguration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle. ∗Working paper. [email protected] – http://maanighaffari.com †Centre for Autonomous Systems (CAS), University of Technology Sydney. ar X iv :1 60 7. 01 88 3v 4 [ cs .R O ] 4 F eb 2 01 7