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

TL;DR

This paper introduces a sampling-based motion planning algorithm that optimizes information gathering for robotic exploration and environmental monitoring, incorporating uncertainty and providing automatic stopping criteria.

Contribution

It presents a novel sampling-based framework with information-theoretic convergence guarantees for incremental informative motion planning.

Findings

Effective in dense map representations

Automatic stopping criterion based on entropy convergence

Validated through robotic exploration and environmental monitoring scenarios

Abstract

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.