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

TL;DR

This paper presents a decentralized multi-UAV framework for cooperative exploration and target detection in complex, GPS-denied environments, demonstrating improved efficiency and success rates through simulation.

Contribution

It introduces a probabilistic decentralized approach for multi-UAV exploration and search in cluttered environments with limited communication.

Findings

Enhanced search efficiency with more UAVs

Higher success rates in target finding

Reduced time-cost in exploration tasks

Abstract

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.