Hybrid A*-BéZier Optimization for 3D Path Planning in Complex EN Vironments

To overcome limitations in traditional A* algorithms for 3D path planning-including path roughness, node redun dancy, and safety concerns-we propose a hybrid strategy integr ating Enhanced Heuristic A* with adaptive Bézier optimization. The approach refines heuristic functions via neural networks to dynamically guide path exploration, reducing redundant nodes a nd improving search efficiency. Concurrently, it employs segmen ted Bézier smoothing with local adaptive control points to ensure path continuity while mitigating collision risks. Simulations conf irm that in medium-to-high density obstacle environments, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{m}$</tex> ethod reduces indexed nodes to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 / 5-1 / 12$</tex> of benchmarks (traditio nal A* + global Bézier smoothing), significantly boosting comput ational efficiency and safety. This provides an effective solution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{f}$</tex> or UUV complex 3D path planning.