To improve the safety and effectiveness of autonomous towing aircraft aboard the carrier deck, this study proposes a velocity-restricted path planner algorithm named as kinodynamic safety optimal rapidly exploring random tree (KS-RRT) to plan a near time-optimal path. First, a speed map is introduced to assign different maximum allowable velocity for the sampling points in the workspace, and the traverse time is calculated along the kinodynamic connection of two sampling points. Then the near time-optimal path in the tree-structured search map can be obtained by the rewiring procedures, instead of a distance-optimal path in the original RRT algorithm. In order to enhance the planner’s performance, goal biasing scheme and fast collision checking technique are adopted in the algorithm. Since the sampling-based methods are sensitive to their parameters, simulation experiments are first conducted to determine the optimal input settings for the specific problem. The effectiveness of the proposed algorithm is validated in several common aircraft parking scenarios. Comparing with standard RRT and human heuristic driving, KS-RRT demonstrates a higher success rate, as well as shorter computation and trajectory time. In conclusion, KS-RRT algorithm is suitable to generate a near time-optimal safe path for autonomous high density parking in semistructured environment.

中文翻译：

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基于动动力学安全RRT的半结构化环境下飞机停放轨迹规划

为了提高航母甲板上自主牵引飞机的安全性和有效性，本研究提出了一种速度受限的路径规划算法，称为动动力学安全最优快速探索随机树（KS-RRT ），以规划近乎时间最优的路径。首先，引入速度图为工作空间中的采样点分配不同的最大允许速度，并沿两个采样点的动动力连接计算遍历时间。然后可以通过重新布线程序获得树结构搜索图中的近时间最优路径，而不是原始RRT中的距离最优路径算法。为了提高规划器的性能，算法中采用了目标偏置方案和快速碰撞检查技术。由于基于采样的方法对其参数敏感，因此首先进行模拟实验以确定特定问题的最佳输入设置。在几种常见的飞机停放场景中验证了所提出算法的有效性。与标准 RRT和人类启发式驾驶相比，KS-RRT展示了更高的成功率，以及更短的计算和轨迹时间。总之，KS-RRT算法适用于为半结构化环境中的自主高密度停车生成近乎时间最优的安全路径。