Sampling-based Incremental Information Gathering with Applications to Robotic Exploration and Environmental Monitoring
This work addresses incremental information gathering for robotic systems, offering a method for autonomous exploration and monitoring tasks.
The authors tackled the problem of robotic exploration and environmental monitoring by proposing a sampling-based motion planning algorithm with an information-theoretic convergence criterion, resulting in an automatic stopping criterion and demonstrated performance in scenarios including robotic exploration and wireless signal strength 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 constrained maximization problem. Our approach is built on rapidly-exploring information gathering algorithms and benefits 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 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.