The intelligent drone is the key device of the future Internet of Drone, and its safe and robust flight in complicated environments still faces challenges. In this paper, we present a sampling-based kinodynamic planning algorithm for quadrotors, which plans a dynamically feasible trajectory in a complicated environment. We have designed a method to constrain the sampling state by using the vector field to construct a cone in the sampling stage of RRT*, so that the generated trajectory is connected as smoothly as possible to other states in the reachable set. The motion primitives are then generated by solving an optimal control problem and an explicit solution of the optimal duration for the motion primitives is given to optimally connect any pair of states. In addition, we have tried a new method to determine the neighbor radius for the non-Euclidean metrics of this paper. Finally, the planned trajectory is applied to the simulated quadrotor, which verifies the dynamic feasibility of the trajectory. Simulation results show that compared with the existing kinodynamic RRT* under the same number of iterations, the proposed algorithm explores more states with a shorter execution time and generates a smoother trajectory.

智能无人机是未来无人机互联网的关键设备，其在复杂环境中的安全可靠飞行仍然面临挑战。在本文中，我们提出了一种基于采样的四旋翼机动力学设计算法，该算法可在复杂环境中规划动态可行的轨迹。我们设计了一种方法，通过在RRT *的采样阶段使用向量场构造圆锥来限制采样状态，以便将生成的轨迹尽可能平滑地连接到可到达集合中的其他状态。然后，通过解决最优控制问题来生成运动图元，并给出运动图元的最佳持续时间的显式解决方案，以最佳地连接任意一对状态。此外，我们尝试了一种新方法来确定本文非欧几里得度量的邻居半径。最后，将计划轨迹应用于仿真的四旋翼飞行器，验证了轨迹的动态可行性。仿真结果表明，与现有的动力学动力学RRT *在相同的迭代次数下相比，该算法在执行时间更短的情况下探索了更多的状态，并生成了更平滑的轨迹。