中文摘要：

针对3维复杂山地环境中执行无碰撞低空飞行任务的旋翼无人飞行器，提出了一种高时效、低代价的航迹规划策略，设计并采用了改进的稀疏A＊算法和生物启发神经动力学模型的融合算法．该算法在稀疏A＊全局优化搜索的基础之上融入生物启发神经动力学模型来调整局部航线以加快最优航迹的形成，并运用神经动力学模型来实时获取和处理环境中的局部动态信息，实现了融合算法的在线规划能力，从而解决了传统最优路径搜索算法无法实现的动态规划的难题．通过在3维空间中设置多峰山地，尤其是凹形山体作为障碍进行仿真实验，实验结果表明，该融合算法不仅降低了A＊算法的复杂度和耗时，而且改善了生物启发神经动力学模型尚未考虑的代价花费问题，更能够在线应对任务空间中的突发威胁，使旋翼无人飞行器在动、静态障碍物相结合的复杂环境下能够规划出一条安全、快速抵达目标点的低代价且优质的航迹．

英文摘要：

A time-efficient and low-cost path planning strategy is proposed by designing and using a fusion algorithm composed of an improved sparse A＊ algorithm and a bio-inspired neural dynamics model, and it is an optimal strategy for a rotorcraft UAV （unmanned aerial vehicle） when performing non-collision flying tasks in three-dimensional low-altitude cluttered mountainous environments. The bio-inspired neural dynamics model is integrated into sparse A＊ global optimal search to adjust local paths in order to speed up the formation of the final optimal path in the proposed fusion algorithm, and the neural dynamics model is adopted to obtain and process local dynamic information from the environment in real time. Therefore, the online path planning is realized by the fusion algorithm, and dynamic path planning problem is solved, which is impossible for the traditional best-first search algorithm. Experiments are carried out in an emulational 3D task space of multi-peak mountainous environment, especially for the concave mountainous environment. Experimental results show that the proposed fusion algorithm not only reduces complexity and time consumption of A＊ algorithm, but also takes the cost of the path into account which isn＇t considered in the bio-inspired neural dynamics model. Furthermore, it can cope with unexpected threats in the task space on line. Then finally, a low-cost and high-quality path is planned out to reach target position safely and quickly for a rotorcraft UAV flying in cluttered environment containing both static and dynamic obstacles.