UC San Diego

This paper explores 3D path planning for unmanned aerial vehicles (UAVs) in 3D point cloud environments. Derivative maps such as dense point clouds, mesh maps, octomaps, etc. are frequently used for path planning purposes. A target-oriented 3D path planning algorithm, directly using point clouds to compute optimized trajectories for an UAV, is presented in this article. 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 connect RRT-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. Following trajectory generation, the algorithm creates way-point based, closed loop quadrotor controls for pitch, roll, and yaw attitude angle as well as dynamics commands for the UAV. Simulations of UAV 3D path planning based on different target points in the point cloud map are presented, showing the effectiveness and feasibility of this approach.