For real applications of unmanned aerial vehicles, the capability of navigating with full autonomy in unknown environments is a crucial requirement. However, planning a shorter path with less computing time is contradictory. To address this problem, we present a framework with the map planner and point cloud planner running in parallel in this paper. The map planner determines the initial path using the improved jump point search method on the 2D map, and then it tries to optimize the path by considering a possible shorter 3D path. The point cloud planner is executed at a high frequency to generate the motion primitives. It makes the drone follow the solved path and avoid the suddenly appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while reacting quickly to the intruding obstacles. We demonstrate fully autonomous quadrotor flight tests in unknown and complex environments with static and dynamic obstacles to validate the proposed method. In simulation and hardware experiments, the proposed framework shows satisfactorily comprehensive performance.
翻译:对于无人驾驶飞行器的实际应用而言,在未知环境中完全自主地航行的能力是一项关键要求。然而,规划一个较短且计算时间较少的路线是自相矛盾的。为了解决这一问题,我们提出了一个框架,由地图规划员和点云规划员在本文中平行运行。地图规划员利用2D地图上的改进跳点搜索方法确定了初始路径,然后它试图通过考虑一个可能的较短的3D路径优化路径来优化路径。点云规划员是高频执行以生成运动原始路径的。它使无人驾驶飞机沿着已解决的道路前进,避免附近突然出现的障碍。因此,飞行器可以在快速应对阻塞障碍的同时实现一个短轨道。我们在未知和复杂的环境中展示完全自主的二次飞行测试,同时设置固定和动态障碍来验证拟议方法。在模拟和硬件实验中,拟议框架显示了令人满意的全面性表现。