# Planning for robotic exploration based on forward simulation

arXiv: https://arxiv.org/abs/1502.02474 · HTML: https://mlsanity.com/paper/1502.02474/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2015.

## Built on by (most cited first, 5 of 5)

- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Monte Carlo Information-Oriented Planning](https://mlsanity.com/paper/2103.11345/) (2021)
- [Opportunistic Collaborative Planning with Large Vision Model Guided Control and Joint Query-Service Optimization](https://mlsanity.com/paper/2504.18057/) (2025)
- [Risk-aware Meta-level Decision Making for Exploration Under Uncertainty](https://mlsanity.com/paper/2209.05580/) (2022)
- [Generative-Model Predictive Planning for Navigation in Partially Observable Environments](https://mlsanity.com/paper/2606.18888/) (2026)

## Abstract (arXiv)

We address the problem of controlling a mobile robot to explore a partially known environment. The robot's objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.