基于改进的 A* 算法和贝塞尔曲线融合的飞行汽车三维路径规划

Jifa Yan, Tao Deng, Binhao Xu
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摘要

飞行汽车是一种新型车辆,具有很强的避障能力,适用于城市空中交通和未来的智能出行。为了规划飞行汽车在城市环境中的可行路径,提出了一种基于改进 A* 算法和贝塞尔曲线融合的飞行汽车三维路径规划策略。首先,改进了 A* 算法的搜索邻域,利用地面模式 9 邻域和低空飞行模式 10 邻域进行节点扩展,快速获取可行路径方案。其次,在启发式函数中考虑不同运动过程的能耗、时间和模式切换损失成本,实现运动路径和运动模式的统一规划。最后,根据规划的路径,使用分段贝塞尔曲线对路径进行平滑处理。结果表明,在复杂地图中,与只考虑能耗的传统车辆相比,该策略有效地减少了 94.5 米的路径长度,降低了 33.1%的加权成本。与综合权衡能耗和时间的策略相比,路径长度减少了 4.31 米,加权成本降低了 13.6%。
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Three dimensional path planning for flying car based on improved A* algorithm and Bezier curve fusion
Flying car is a new type of vehicle with high obstacle avoidance ability for urban air traffic and future smart travel. In order to plan feasible paths for flying cars in urban environments, a three dimensional path planning strategy for flying cars based on the fusion of improved A* algorithm and Bezier curves is proposed. Firstly, the search neighborhood of the A* algorithm is improved, and the node expansion is carried out by using the ground mode 9 neighborhood and the low-altitude flight mode 10 neighborhood to quickly obtain feasible path options. Secondly, the energy consumption, time and mode switching loss cost of different motion processes are considered in the heuristic function to achieve unified planning of motion paths and motion modes. Finally, the path is smoothed using piecewise Bezier curves according to the planned path. The results show that in complex maps, compared with traditional vehicles that only consider energy consumption, this strategy effectively reduces the path length by 94.5 m and reduces the weighted cost by 33.1%. Compared with the strategy that comprehensively weighs energy consumption and time, the path length is reduced by 4.31 m and the weighted cost is reduced by 13.6%.
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