基于流水避石原理的无人机三维航路规划方法
收稿日期: 2012-08-01
修回日期: 2013-01-07
网络出版日期: 2013-01-22
基金资助
国家自然科学基金(61175084)
Three-dimensional Path Planning for Unmanned Aerial Vehicles Based on Principles of Stream Avoiding Obstacles
Received date: 2012-08-01
Revised date: 2013-01-07
Online published: 2013-01-22
Supported by
National Natural Science Foundation of China (61175084)
借鉴自然界流水避石现象,提出一种基于流体计算的无人机(UAV)三维(3D)航路规划方法。首先介绍了球心位于坐标原点时,球形障碍三维绕流问题的解析解。之后采用旋转平移矩阵与流线数据叠加方法生成了任意位置多障碍同时存在的三维流线。为验证解析解的有效性同时给出该方法基于数值模拟的计算过程,对适合无人机三维航路规划的流体模型和数值求解方法进行了分析,并给出了通过数值模拟求解航路的方法。最后,根据无人机机动约束对流线进行处理得到可飞航路,将航路长度、纵向和横侧向机动次数作为子目标函数对航路进行综合评价。仿真结果表明:解析法航路规划中,圆球障碍的地形建模简单计算量小,航路集中在由起点至终点的航路带间;数值法航路规划适合障碍分布复杂的地形,航路分布于规划空间中。这两种方法的航路平滑,能够满足无人机飞行约束,航路具有绕流意义的最优性,可以避免势场法的局部极小问题,并且可以提供多条备选航路。
梁宵, 王宏伦, 李大伟, 吕文涛 . 基于流水避石原理的无人机三维航路规划方法[J]. 航空学报, 2013 , 34(7) : 1670 -1681 . DOI: 10.7527/S1000-6893.2013.0061
Using the principles of fluid computation, a three-dimensional (3D) path planning method for unmanned aerial vehicles (UAVs) is studied by imitating the natural phenomenon of a flowing stream avoiding obstacles. First, an analytical solution of the steady 3D ideal flow acting on a single spherical obstacle is used to imitate the movement of a UAV. Then, a rotation-translation matrix in combination with the stream data are designed to generate streamlines when there are multiple obstacles in arbitrary positions. To verify the effectiveness of the method and introduce the method of numerical simulation, the fluid model and numerical solution suitable for 3D path planning are analyzed. Finally, the streamlines that satisfy the maneuverability constraints of the UAV are selected as the flight paths. Length of the path and times of motion in longitudinal and latitudinal directions are chosen as sub-objective functions to make a comprehensive evaluation. Simulation results demonstrate that in analytical paths, the model of spherical obstacles will reduce computation, and paths distribute in a ribbon from the starting to the finishing area; the numerical paths can deal with complex terrain, and paths distribute in a planned space. Both methods based on fluid flow can avoid local minima of a potential field, satisfy UAV constraints and provide multiple alternative paths. In addition, the paths are smooth and have the optimal characteristic of flow around obstacles.
[1] Beard R W, McLain T W, Goodrich M A, et al. Coordinated target assignment and intercept for unmanned air vehicles. IEEE Transactions on Robotics and Automation, 2002, 18(6): 911-922.
[2] Yang J, Dymond P, Jenkin M. Practicality-based probabilistic roadmaps method. 2011 Canadian Conference on Computer and Robot Vision (CRV). Piscataway: IEEE Press, 2011: 102-108.
[3] Zucker M, Kuffner J, Branicky M. Multipartite RRTs for rapid replanning in dynamic environments. 2007 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2007: 1603-1609.
[4] Yang H I, Zhao Y J. Trajectory planning for autonomous aerospace vehicles amid known obstacles and conflicts. Journal of Guidance, Control, and Dynamics, 2004, 27(6): 997-1008.
[5] Nikolos I K, Valavanis K P, Tsourveloudis N C, et al. Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2003, 33(6): 898-912.
[6] Anderson M B, Lopez J L, Evers J H. A comparison of trajectory determination approaches for small UAVs. AIAA-2006-6644, 2006.
[7] Darrah M A, Niland W M, Stolarik B M. Increasing UAV task assignment performance through parallelized genetic algorithms. AIAA-2007-2815, 2007.
[8] Swartzentruber L, Foo J L, Winer E H. Three-dimensional multi-objective UAV path planner using meta-paths for decision making and visualization. AIAA-2008-5830, 2008.
[9] Swartzentruber L, Foo J L, Winer E H. Three-dimensional multi-objective UAV path planner using terrain information. AIAA-2009-2222, 2009.
[10] Foo J L, Knutzon J, Kalivarapu V, et al. Path planning of unmanned aerial vehicles using B-splines and particle swarm optimization. Journal of Aerospace Computing, Information, and Communication, 2009, 6(4): 271-290.
[11] Sullivan J, Waydo S, Campbell M. Using stream functions for complex behavior and path generation. AIAA-2003-5800, 2003.
[12] Philip J P. Fluid mechanics: SI version. 8th International student edition. Washington D.C.: Wiley, 2011: 34-40.
[13] Zhu Y, Zhang T, Song J Y. Study on the local minima problem of path planning using potential field method in unknown environments. Acta Automatica Sinica, 2010, 36(8): 1122-1130. (in Chinese) 朱毅, 张涛, 宋靖雁. 未知环境下势场法路径规划的局部极小问题研究. 自动化学报, 2010, 36(8): 1122-1130.
[14] Atefi G H, Niazmand H, Meigounpoory M R. Numerical analysis of 3-D flow past a stationary sphere with slip condition at low and moderate reynolds numbers. Journal of Dispersion Science and Technology, 2007, 28(4): 591-602.
[15] Zhang Y F, Chen H X, Fu S, et al. A practical optimization design method for transport aircraft wing/nacelle integration. Acta Aeronautica et Astronautica Sinica, 2012, 33(11): 1993-2001. (in Chinese) 张宇飞, 陈海昕, 符松, 等. 一种实用的运输类飞机机翼/发动机短舱一体化优化设计方法. 航空学报, 2012, 33(11): 1993-2001.
[16] Gu L, Qi H T, Liu H J, et al. Numerical simulation on wind load characteristics and wind environment for Olympic Park Tennis Centre Stadium. Journal of Building Structures, 2009, 30(3): 134-143. (in Chinese) 顾磊, 齐宏拓, 刘红军, 等. 奥运网球中心赛场风荷载和风环境数值模拟分析. 建筑结构学报, 2009, 30(3): 134-143.
[17] Chen J Q, Zhang Y R. Verification and validation in CFD based on the Richardson extrapolation method. Acta Aerodynamica Sinica, 2012, 30(2): 176-183. (in Chinese) 陈坚强, 张益荣. 基于Richardson插值法的CFD验证和确认方法研究. 空气动力学学报, 2012, 30(2): 176-183.
[18] Wang F J. Computational fluid dynamics analysis—principles and applications of CFD. Beijing: Tsinghua University Press, 2010: 57-60. (in Chinese) 王福军. 计算流体动力学分析——CFD软件原理与应用. 北京: 清华大学出版社, 2010: 57-60.
/
| 〈 |
|
〉 |
CJA