This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT^#. MP-RRT^# joins the potentialities of RRT^# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT^# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT^# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT^# approach.

本文介绍了一种用于无人机系统 (UAS) 的运动动力学运动规划算法，称为 MP-RRT ^#。MP-RRT ^＃加入的RRT的潜力^＃基于模型预测控制来有效地解决在差动限制运动规划问题的策略。与其他基于 RRT 的算法类似，MP-RRT ^#探索构建渐近最优图的地图。在每次迭代中，图都会在 UAS 的参考状态下使用新的顶点进行扩展。然后，使用模型预测控制策略执行前向仿真以评估两个相邻顶点之间的运动，并计算状态空间中的轨迹。结果，MP-RRT ^#算法最终为满足动态约束的 UAS 生成可行的轨迹。使用 PX4 自动驾驶仪控制的模拟无人机获得的模拟结果证实了 MP-RRT ^#方法的有效性。