An Approach for Multi-UAV System Navigation and Target Finding in Cluttered Environments

The range of applications of unmanned aerial vehicles (UAVs) could be widened if a team of multiple UAVs are used. In this paper, we propose a framework of a team of UAVs with the aim of cooperatively finding a target in a real-world based environment with obstacles. Examples of such applications include search and rescue, remote sensing or infrastructure inspection, which can benefit from an efficient and cooperative multi-UAV system. The framework presented in this paper is modified and extended based on Partially Observable Markov Decision Processes (POMDP) to suit the decentralised multi-agent system while considering the necessary uncertainties of environments and localisations. In addition, the team can cooperate efficiently by sharing limited observation in the mission. We simulated the system in Gazebo simulator and tested the performances for an increased number of UAVs in a cluttered flying area. Results indicate that a POMDP formulation allows for uncertainty in observations and multi-agent navigation and target finding can be implemented in a real-time application in real-world based scenarios.