AI-based Cooperative Control for Multi UAVs for Search and Rescue Missions

Search and rescue (SAR) missions pose great challenges to ground teams due to the need to cover extensive areas and explore complex and dangerous terrains. Multi-UAVs systems offer significant advantages enabling efficient coverage of large areas while providing ground teams with real-time monitoring and detection

thus, ultimately reducing the overall operation time, increasing its effectiveness and success and making it cost effective. However, the success in using multi-UAVs systems in SAR missions rely heavily on the cooperative control strategies adopted for coverage, dynamic exploration and task allocation coordinated in a central manner via a common ground station or distributed among the UAVs. Moreover, considering the UAVs sensory capability in terms of onboard vision-based cameras.

This project aims to address one or more of the following research objectives for target search and detection:

Coordinated coverage path planning for multi-UAVs systems employing centralised and distributed control mechanisms.

Dynamic and efficient area exploration adopting search-based and graph-based techniques.

Task allocation and mission planning using agent-based deep learning techniques.