# A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments

arXiv: https://arxiv.org/abs/2107.08834 · HTML: https://mlsanity.com/paper/2107.08834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions](https://mlsanity.com/paper/1707.07399/) (2017)
- [Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions](https://mlsanity.com/paper/1810.09729/) (2018)
- [Search and Rescue under the Forest Canopy using Multiple UAVs](https://mlsanity.com/paper/1908.10541/) (2019)

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

- [A Search and Detection Autonomous Drone System: from Design to Implementation](https://mlsanity.com/paper/2211.15866/) (2022)

## Abstract (arXiv)

The use of multi-rotor Unmanned Aerial Vehicles (UAVs) for search and rescue as well as remote sensing is rapidly increasing. Multi-rotor UAVs, however, have limited endurance. The range of UAV applications can be widened if teams of multiple UAVs are used. We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Examples of such environments include indoor scenarios, urban or natural canyons, caves, and tunnels, where the GPS signal is limited or blocked. The framework is based on a probabilistic decentralised Partially Observable Markov Decision Process which accounts for the uncertainties in sensing and the environment. The team can cooperate efficiently, with each UAV sharing only limited processed observations and their locations during the mission. The system is simulated using the Robotic Operating System and Gazebo. Performance of the system with an increasing number of UAVs in several indoor scenarios with obstacles is tested. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.