# Multi-Agent Active Search using Detection and Location Uncertainty

arXiv: https://arxiv.org/abs/2203.04524 · HTML: https://mlsanity.com/paper/2203.04524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Probabilistic Object Detection: Definition and Evaluation](https://mlsanity.com/paper/1811.10800/) (2018)
- [Active Object Localization with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06015/) (2015)
- [Inferring Spatial Uncertainty in Object Detection](https://mlsanity.com/paper/2003.03644/) (2020)
- [Near-Optimal Adaptive Compressed Sensing](https://mlsanity.com/paper/1306.6239/) (2013)
- [Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras](https://mlsanity.com/paper/1701.05748/) (2017)
- [A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks](https://mlsanity.com/paper/1909.05214/) (2019)
- [Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments](https://mlsanity.com/paper/1909.12216/) (2019)
- [Active Search for Sparse Signals with Region Sensing](https://mlsanity.com/paper/1612.00583/) (2016)
- [Multi-Modal Active Perception for Information Gathering in Science Missions](https://mlsanity.com/paper/1712.09716/) (2017)
- [Multi-Agent Active Search using Realistic Depth-Aware Noise Model](https://mlsanity.com/paper/2011.04825/) (2020)
- [Intrinsic Calibration of Depth Cameras for Mobile Robots using a Radial Laser Scanner](https://mlsanity.com/paper/1907.01839/) (2019)
- [UGV-UAV Object Geolocation in Unstructured Environments](https://mlsanity.com/paper/2201.05518/) (2022)

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

- [Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment](https://mlsanity.com/paper/2306.11301/) (2023)

## Abstract (arXiv)

Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty. The more common approach in robotics is to focus on location uncertainty and remove detection uncertainty by thresholding the detection probability to zero or one. In contrast, it is common in the sparse signal processing literature to assume the target location is accurate and instead focus on the uncertainty of its detection. In this work, we first propose an inference method to jointly handle both target detection and location uncertainty. We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search. We perform simulation experiments to show that our algorithms outperform competing baselines that only account for either target detection or location uncertainty. We finally demonstrate the real world transferability of our algorithms using a realistic simulation environment we created on the Unreal Engine 4 platform with an AirSim plugin.