# GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

arXiv: https://arxiv.org/abs/2304.02075 · HTML: https://mlsanity.com/paper/2304.02075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Multiple Anchor Learning for Visual Object Detection](https://mlsanity.com/paper/1912.02252/) (2019)
- [Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots](https://mlsanity.com/paper/1807.11174/) (2018)
- [A Coalition Formation Algorithm for Multi-Robot Task Allocation in Large-Scale Natural Disasters](https://mlsanity.com/paper/1704.05905/) (2017)
- [Active Search for Sparse Signals with Region Sensing](https://mlsanity.com/paper/1612.00583/) (2016)
- [Ergodic Coverage In Constrained Environments Using Stochastic Trajectory Optimization](https://mlsanity.com/paper/1707.04294/) (2017)
- [Multi-Agent Active Search using Realistic Depth-Aware Noise Model](https://mlsanity.com/paper/2011.04825/) (2020)
- [Sparse Bayesian Learning with Dynamic Filtering for Inference of Time-Varying Sparse Signals](https://mlsanity.com/paper/1902.05362/) (2019)

## Built on by (most cited first, 1 of 1)

- [Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents](https://mlsanity.com/paper/2401.03154/) (2024)

## Abstract (arXiv)

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75,000 sq. m. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline.