泡泡一分钟：Motion Planning for a Small Aerobatic Fixed-Wing Unmanned Aerial Vehicle

Motion Planning for a Small Aerobatic Fixed-Wing Unmanned Aerial Vehicle

Joshua Levin, Aditya Paranjape, and Meyer Nahon

小型特技飞行无人机的运动规划

https://pan.baidu.com/s/1xB6WxNMEo-SNAApsNT0GQQ

Abstract— A motion planner is developed for guiding a small aerobatic fixed-wing unmanned aerial vehicle to a desired goal region in a highly constrained, three-dimensional, known environment with static obstacles. The planner is based on the Rapidly-Exploring Random Trees (RRT) algorithm, and pieces together feasible trajectories from a library of motion primitives. Among other more conventional motion primitives,the library includes three extreme maneuvers: a cruise-to-hover transition, a hover-to-cruise transition, and an aggressive turn-around. The algorithm is efficient; it can be run in real-time to rapidly generate a plan starting from the aircraft’s configuration at run-time. The motion planner is closely coupled to a feedback controller. Simulations using an aircraft dynamics model demonstrate the effectiveness of the system to guide and control the aircraft to a desired goal region. Preliminary flight test results are also presented.

在本文中，我们开发了一种运动规划器，用于在具有静态障碍物的高度受限的三维已知环境中将小型特技飞行固定翼无人驾驶飞行器引导至期望目标区域。该规划器基于快速探索随机树（RRT）算法，并将来自运动图元库的可行轨迹拼接在一起。在其他更传统的运动图元中，该库包括三种极端机动：巡航到悬停过渡，悬停到巡航过渡以及积极的转向。该算法是高效的; 它可以实时运行，以便在运行时从飞机的配置开始快速生成规划。运动规划器与反馈控制器紧密耦合。使用飞机动力学模型的模拟证明了该系统将飞机引导和控制到期望目标区域的有效性。 初步的飞行测试结果也被提供了。

控制器的任务是跟踪运动规划。它接收树节点并参考运动图元库以将其数据解释为全状态时间相关轨迹和前馈控制输入。完整的控制系统将前馈输入与跟踪参考轨迹偏差的反馈控制法相结合。

三维运动规划跟踪