Design and Development Path Planning of Unmanned Aerial Vehicles for Stealth Missions in Dangerous Environments
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Abstract
Today, unmanned aerial vehicles (UAVs) play an important role in military applications. The development of UAVs to perform complex and dangerous missions is critical. Effective design and path planning are essential for enabling UAVs to undertake such missions. This research proposes a path-planning algorithm for UAVs to automatically perform stealth missions in hazardous environments. The proposed algorithm is intended for indoor applications. The design of the path-planning algorithm in this research is based on the A* algorithm developed by Ren Tianzhu. This algorithm was modified using elements from the Lifelong Planning A* and D* Lite algorithms, developed by Prof. Sven Koenig and Prof. Maxim Likhachev, to address both static and dynamic obstacles and threats. All three path-planning algorithms were evaluated through simulations using a Python program. The results show that the D* Lite-based algorithm typically results in a path length that is 4% shorter on average compared to the Lifelong Planning A* algorithm, but 1.5% longer on average compared to the A* algorithm developed by Ren. Additionally, the execution time for the D* Lite-based algorithm is on average 530.8% less than that of the Lifelong Planning A* algorithm, but 14.7% more compared to the A* algorithm developed by Ren. Despite this, the A* algorithm developed by Ren cannot find paths that avoid dynamic obstacles or threats. In contrast, both the D* Lite and Lifelong Planning A* algorithms can effectively navigate around such dynamic obstacles and threats. Overall, the D* Lite-based path planning demonstrates superior performance compared to both the A* and Lifelong Planning A* algorithms. It is well-suited for stealth missions in hazardous environments that require autonomous operation and the ability to avoid both static and dynamic threats.
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