# MEXGEN: An Effective and Efficient Information Gain Approximation for Information Gathering Path Planning

arXiv: https://arxiv.org/abs/2405.02605 · HTML: https://mlsanity.com/paper/2405.02605/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 9 of 9)

- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty](https://mlsanity.com/paper/1802.06215/) (2018)
- [SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain](https://mlsanity.com/paper/2209.11034/) (2022)
- [Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach](https://mlsanity.com/paper/1807.08229/) (2018)
- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)
- [Belief Space Planning for Mobile Robots with Range Sensors using iLQG](https://mlsanity.com/paper/2102.05466/) (2021)
- [TrackerBots: Autonomous Unmanned Aerial Vehicle for Real-Time Localization and Tracking of Multiple Radio-Tagged Animals](https://mlsanity.com/paper/1712.01491/) (2017)
- [ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains](https://mlsanity.com/paper/2308.08104/) (2023)

## Abstract (arXiv)

Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realizing autonomy demands online planning algorithms to solve sequential decision making problems under uncertainty; because, objects of interest are often dynamic, object state, such as location is not directly observable and are obtained from noisy measurements. Such planning problems are notoriously difficult due to the combinatorial nature of predicting the future to make optimal decisions. For information theoretic planning algorithms, we develop a computationally efficient and effective approximation for the difficult problem of predicting the likely sensor measurements from uncertain belief states}. The approach more accurately predicts information gain from information gathering actions. Our theoretical analysis proves the proposed formulation achieves a lower prediction error than the current efficient-method. We demonstrate improved performance gains in radio-source tracking and localization problems using extensive simulated and field experiments with a multirotor aerial robot.