Abstract
Unmanned aerial vehicle (UAV) path planning in a hostile environment (UPPHE) considers the features and information of defense systems to plan a risk-constrained shortest path for a UAV from the initial position to target. When the characteristics of defense systems cannot be accurately expressed manually, it will be brutal to effectively or efficiently implement the classical planning methods because of the lack of risk information to constrain and guide planning. We present a data-driven approach for UPPHE, the feedback rapidly-exploring random tree star algorithm (FRRT*), with a data-driven risk network and feedback module. FRRT* can use information extracted from situation data to constrain the growth of the random tree and make biased adjustments to improve planning efficiency. Simulation experiments verify the effectiveness of the risk network in guiding planning, and the improved efficiency brought by the feedback module.
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