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

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.

Revision submitted to IJRR, 49 pages

References in corpus (3)

• Efficient Informative Sensing using Multiple Robots
• Gaussian Process Autonomous Mapping and Exploration for Range Sensing Mobile Robots
• Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs

Cited by in corpus (8)

• Asymptotically Optimal Sampling-Based Motion Planning Methods
• Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
• Energy-based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning
• Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
• CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization
• Enough is Enough: Towards Autonomous Uncertainty-driven Stopping Criteria
• Multi-vehicle Dynamic Water Surface Monitoring
• An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics