%0 Journal Article
%T Sampling
%A Gamini Dissanayake
%A Jaime Valls Miro
%A Maani Ghaffari Jadidi
%J The International Journal of Robotics Research
%@ 1741-3176
%D 2019
%R 10.1177/0278364919844575
%X 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 constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the 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 an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration 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
%K Motion and path planning
%K planning under uncertainty
%K belief space planning
%K field robotics
%K robotic information gathering
%K environmental monitoring
%K robotic exploration
%K Gaussian processes
%K mutual information
%U https://journals.sagepub.com/doi/full/10.1177/0278364919844575