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

arXiv: https://arxiv.org/abs/1607.01883 · HTML: https://mlsanity.com/paper/1607.01883/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2016.

## Built on by (most cited first, 3 of 3)

- [Energy-based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2207.03034/) (2022)
- [Feature Space Exploration For Planning Initial Benthic AUV Surveys](https://mlsanity.com/paper/2105.11598/) (2021)
- [CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization](https://mlsanity.com/paper/2309.05200/) (2023)

## Abstract (arXiv)

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.