Over the past few years, the use of Unmanned Aerial Vehicles (UAVs) has increased because of their greater adaptability and reduced mission cost. Recent developments in drone technology and the increased complexity of mission environment, have led to the use of multiple UAVs. Considering the dynamics of an uncertain environment, a Decentralized Cooperative Path Planning algorithm has been implemented in this paper which allows each agent to coordinate effectively in an area where communication with Ground Control Station is constrained. An Ant Colony Optimization based algorithm has been implemented for multiple UAVs to cooperatively search a given area. The developed algorithm incorporates manoeuvrability, collision avoidance, and threat avoidance constraints. An overall efficient path for each UAV is planned by using State Transformation Rule and Pheromone Update Mechanism at each time interval. An improved heuristic has been proposed as an improvement to the developed algorithm to reduce the manoeuvring cost of UAVs. Generated paths for each UAV have been validated by using Hardware in the Loop testing on multiple UAVs by using Dronekit-Python and Mission Planner. Results show that the use of multiple UAVs is effective for path planning in surveillance missions as it increases the coverage efficiency and reduces the overall mission time.
Target Search Through Decentralized and Co-operative Surveillance of Multiple UAVs in an Uncertain Environment
International Conference on Aeronautical Sciences, Engineering and Technology ; 2023 ; Muscat, Oman October 03, 2023 - October 05, 2023
26.12.2023
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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