论文标题
BTO-RRT：一种快速，最佳，平滑和基于点云的路径计划算法
BTO-RRT: A rapid, optimal, smooth and point cloud-based path planning algorithm
论文作者
论文摘要
本文探讨了在点云环境中的机器人（例如自动驾驶汽车）的快速，最佳的平滑路径规划算法。衍生图图，例如密集点云，网格地图，Octomaps等，经常用于路径计划。本文介绍了直接使用点云来计算优化且动态可行的轨迹的双向目标计划算法。这种方法使用经过改进的基于基于K-D树的障碍避免策略和三步优化的方法，通过分析目标环境的点云来搜索无障碍，低计算成本，平滑和动态可行的路径。此提出的方法绕过了通用的3D MAP离散化，直接利用点云数据，并且可以分为两个部分：基于RRT的修改算法核心和三步优化。给出了具有不同配置和特征的8个2D地图上的模拟，以显示所提出算法的效率和2D性能。本文还显示了与其他基于RRT的算法（如RRT，B-RRT和RRT Star）的基准比较和评估。最后，拟议的算法在三个具有不同密度的3D点云图上成功实现了不同级别的任务目标。整个仿真证明，与其他算法相比，我们的算法不仅可以在2D地图上取得更好的性能，而且还可以在不同的3D点云图上处理不同的任务（地面车辆和无人机应用），这显示了拟议算法的高性能和鲁棒性。该算法是在\ url {https://github.com/zhz03/bto-rrt}开源的
This paper explores a rapid, optimal smooth path-planning algorithm for robots (e.g., autonomous vehicles) in point cloud environments. Derivative maps such as dense point clouds, mesh maps, Octomaps, etc. are frequently used for path planning purposes. A bi-directional target-oriented point planning algorithm, directly using point clouds to compute the optimized and dynamically feasible trajectories, is presented in this paper. This approach searches for obstacle-free, low computational cost, smooth, and dynamically feasible paths by analyzing a point cloud of the target environment, using a modified bi-directional and RRT-connect-based path planning algorithm, with a k-d tree-based obstacle avoidance strategy and three-step optimization. This presented approach bypasses the common 3D map discretization, directly leveraging point cloud data and it can be separated into two parts: modified RRT-based algorithm core and the three-step optimization. Simulations on 8 2D maps with different configurations and characteristics are presented to show the efficiency and 2D performance of the proposed algorithm. Benchmark comparison and evaluation with other RRT-based algorithms like RRT, B-RRT, and RRT star are also shown in the paper. Finally, the proposed algorithm successfully achieved different levels of mission goals on three 3D point cloud maps with different densities. The whole simulation proves that not only can our algorithm achieves a better performance on 2D maps compared with other algorithms, but also it can handle different tasks(ground vehicles and UAV applications) on different 3D point cloud maps, which shows the high performance and robustness of the proposed algorithm. The algorithm is open-sourced at \url{https://github.com/zhz03/BTO-RRT}
