Vector Field Guided RRT* Based on Motion Primitives for Quadrotor Kinodynamic Planning

被引：0
作者：
Zhiling Tang
Bowei Chen
Rushi Lan
Simin Li
机构：
[1] Guilin University of Electronic Technology,School of Information and Communication
[2] Guilin University of Electronic Technology,School of Electronic Engineering and Automation
[3] Guilin University of Electronic Technology,School of Computer and Information Security
来源：
Journal of Intelligent & Robotic Systems | 2020年 / 100卷
关键词：
Kinodynamic planning; Motion primitives; Optimal control; Quadrotor; Vector field;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1325 / 1339
页数：14
相关论文
共 42 条
  • [1] Gharibi M(2016)Internet of drones IEEE Access 4 1148-1162
  • [2] Boutaba R(2017)Flying IoT: Toward low-power vision in the sky IEEE Micro 37 40-51
  • [3] Waslander SL(2018)Dronetrack: Cloud-based real-time object tracking using unmanned aerial vehicles IEEE Access 6 13810-13824
  • [4] Genc H(2018)Universal path planning for an indoor drone Autom. Constr. 95 275-283
  • [5] Zu Y(2012)Optimal control and applications to aerospace: Some results and challenges J. Optim. Theory Appl. 154 713-758
  • [6] Chin TW(2011)Sampling-based algorithms for optimal motion planning Int. J. Robot. Res. 30 846-894
  • [7] Koubaa A(2015)Motion primitives and 3d path planning for fast flight through a forest Int. J. Robot. Re. 34 357-377
  • [8] Qureshi B(2018)Search-based motion planning for aggressive flight in SE(3) IEEE Robot. Autom. Lett. 3 2439-2446
  • [9] Li F(2014)Randomized path planning on vector fields Int. J. Robot. Res. 33 1664-1682
  • [10] Zlatanova S(2014)Time-optimal path planning in dynamic flows using level set equations: theory and schemes Ocean Dyn. 64 1373-1397
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