固定翼无人机在线航迹规划方法

doi:10.11772/j.issn.1001-9081.2019050863

计算机应用 ›› 2019, Vol. 39 ›› Issue (12): 3522-3527.DOI: 10.11772/j.issn.1001-9081.2019050863

固定翼无人机在线航迹规划方法

刘佳^1,2, 秦小林^1,2, 许洋^1,2, 张力戈^1,2

1. 中国科学院成都计算机应用研究所, 成都 610041;
2. 中国科学院大学计算机与控制学院, 北京 100049

收稿日期:2019-05-22 修回日期:2019-07-23 出版日期:2019-12-10 发布日期:2019-07-31
作者简介:刘佳(1995-),女,宁夏银川人,硕士研究生,主要研究方向:无人机航迹规划、机器学习;秦小林(1980-),男,重庆人,研究员,博士生导师,博士,主要研究方向:自动推理、集群智能;许洋(1994-),男,重庆人,硕士研究生,主要研究方向:机器学习、优化算法;张力戈(1995-),男,山西原平人,博士研究生,主要研究方向:机器学习、优化算法。
基金资助:
国家自然科学基金资助项目（61402537）；中国科学院"西部青年学者"项目；四川省科技计划项目（2018GZDZX0041）。

On-line path planning method of fixed-wing unmanned aerial vehicle

LIU Jia^1,2, QIN Xiaolin^1,2, XU Yang^1,2, ZHANG Lige^1,2

1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-05-22 Revised:2019-07-23 Online:2019-12-10 Published:2019-07-31
Contact: 秦小林
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61402537), the Light of West China Program of Chinese Academy of Sciences, the Sichuan Science and Technology Program (2018GZDZX0041).

摘要/Abstract

摘要： 在不确定环境下，针对固定翼无人机（UAV）航迹规划问题，提出了一种基于滚动时域控制的模糊粒子群优化算法与改进人工势场法相结合的在线航迹规划方法。首先，对凸多边形障碍物进行最小外接圆拟合；然后，根据静态威胁，将规划问题转化为一系列时域窗口内的在线子问题，利用模糊粒子群算法实时优化求解以实现静态避障；当环境中存在动态威胁时，使用改进人工势场法对航迹进行调整完成动态避障。为了满足固定翼无人机的动态约束，同时提出固定翼UAV的碰撞检测法，可提前判断障碍物是否为真正威胁源，以此减少转弯频率和幅度，降低飞行代价。仿真实验结果表明，所提方法在固定翼UAV航迹规划中能有效提升规划速度、稳定性与实时避障能力，且克服了传统人工势场容易陷入局部最优的缺点。

关键词: 滚动时域控制, 模糊粒子群, 人工势场, 固定翼无人机, 航迹规划

Abstract: By the combination of fuzzy particle swarm optimization algorithm based on receding horizon control and improved artificial potential field, an on-line path planning method for achieving fixed-wing Unmanned Aerial Vehicle (UAV) path planning in uncertain environment was proposed. Firstly, the minimum circumscribed circle fitting was performed on the convex polygonal obstacles. Then, aiming at the static obstacles, the path planning problem was transformed into a series of on-line sub-problems in the time domain window, and the fuzzy particle swarm algorithm was applied to optimize and solve the sub-problems in real time, realizing the static obstacle avoidance. When there were dynamic obstacles in the environment, the improved artificial potential field was used to accomplish the dynamic obstacle avoidance by adjusting the path. In order to meet the dynamic constraints of fixed-wing UAV, a collision detection method for fixed-wing UAV was proposed to judge whether the obstacles were real threat sources or not in advance and reduce the flight cost by decreasing the turning frequency and range. The simulation results show that, the proposed method can effectively improve the planning speed, stability and real-time obstacle avoidance ability of fixed-wing UAV path planning, and it overcomes the shortcoming of easy to falling into local optimum in traditional artificial potential field method.

Key words: receding horizon control, fuzzy particle swarm, artificial potential field, fixed-wing Unmanned Aerial Vehicle (UAV), path planning

中图分类号:

V249
TP301

刘佳, 秦小林, 许洋, 张力戈. 固定翼无人机在线航迹规划方法[J]. 计算机应用, 2019, 39(12): 3522-3527.

LIU Jia, QIN Xiaolin, XU Yang, ZHANG Lige. On-line path planning method of fixed-wing unmanned aerial vehicle[J]. Journal of Computer Applications, 2019, 39(12): 3522-3527.

参考文献

[1] BORTOFF S A. Path planning for UAVs[C]//Proceedings of the 2000 American Control Conference. Piscataway:IEEE, 2000:364-368.
[2] BHATTACHARYA P, GAVRILOVA M L. Roadmap-based path planning-using the Voronoi diagram for a clearance-based shortest path[J]. IEEE Robotics and Automation Magazine, 2008, 15(2):58-66.
[3] LADD A M, KAVRAKI L E. Measure theoretic analysis of probabilistic path planning[J]. IEEE Transactions on Robotics and Automation, 2004, 20(2):229-242.
[4] SCHOLER F, la COUR-HARBO A, BISGAARD M. Configuration space and visibility graph generation from geometric workspaces for UAVs[C]//Proceedings of the 2011 AIAA Guidance, Navigation, and Control Conference. Reston:AIAA, 2011:Article No. 6416.
[5] WILBURM J, PERHINSCHI M G, WILBURN B, et al. Development of a modified Voronoi algorithm for UAV path planning and obstacle avoidance[C]//Proceedings of the 2012 AIAA Guidance, Navigation, and Control Conference. Reston:AIAA, 2012:Article No. 4904.
[6] CHEN Y, LUO G, MEI Y, et al. UAV path planning using artificial potential field method updated by optimal control theory[J]. International Journal of Systems Science, 2016, 47(6):1407-1420.
[7] DONG Z, CHEN Z, ZHOU R, et al. A hybrid approach of virtual force and A^* search algorithm for UAV path re-planning[C]//Proceedings of the 6th Industrial Electronics and Applications. Piscataway:IEEE, 2011:1140-1145.
[8] WANG H, LYU W, YAO P, et al. Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system[J]. Chinese Journal of Aeronautics, 2015, 28(1):229-239.
[9] 姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016,31(4):701-708.(YAO P, WANG H L. Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer[J]. Control and Decision, 2016, 31(4):701-708.)
[10] 姚远,周兴社,张凯龙,等.基于稀疏A^* 搜索和改进人工势场的无人机动态航迹规划[J].控制理论与应用,2010,27(7):953-959.(YAO Y, ZHOU X S, ZHANG K L, et al. Dynamic trajectory planning for unmanned aerial vehicle based on sparse A^* search and improved artificial potential field[J]. Control Theory and Applications, 2010, 27(7):953-959.)
[11] FAN K C, LUI P C. Solving find path problem in mapped environments using modified A^* algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1994, 24(9):1390-1396.
[12] CHEN G, CRUZ J B. Genetic algorithm for task allocation in UAV cooperative control[C]//Proceedings of the 2003 AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston:AIAA, 2003:Article No. 5582.
[13] SZCZERBA R J, GALKOWSKI P, GLICKTEIN I S, et al. Robust algorithm for real-time route planning[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3):869-878.
[14] KUO P H, LI T H S, CHEN G Y, et al. A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller[J]. The Knowledge Engineering Review, 2017, 32:Article No. e5.
[15] 方群,徐青.基于改进粒子群算法的无人机三维航迹规划[J].西北工业大学学报,2017,35(1):66-73.(FANG Q, XU Q. 3D route planning for UAV based on improved PSO algorithm[J]. Journal of Northwestern Polytechnical University, 2017, 35(1):66-73.)
[16] 王文彬,秦小林,张力戈,等.基于滚动时域的无人机动态航迹规[J].智能系统学报,2018,13(4):524-533.(WANG W B, QIN X L, ZHANG L G, et al. Dynamic UAV trajectory planning based on receding horizon[J]. CAAI Transactions on Intelligent Systems, 2018, 13(4):524-533.)
[17] MAYNE D Q, RAWLINGS J B, RAO C V, et al. Constrained model predictive control:Stability and optimality[J]. Automatica, 2000, 36(6):789-814.
[18] KUWATA Y, RICHARDS A, SCHOUWENAARS T, et al. Decentralized robust receding horizon control for multi-vehicle guidance[C]//Proceedings of the 2006 American Control Conference. Piscataway:IEEE, 2006:2047-2052.
[19] THANH H L N N, PHI N N, HONG S K, et al. Simple nonlinear control of quadcopter for collision avoidance based on geometric approach in static environment[J]. International Journal of Advanced Robotic Systems, 2018, 15(2):1-7.
[20] 张长胜,孙吉贵,欧阳丹彤.一种自适应离散粒子群算法及其应用研究[J].电子学报,2009,37(2):299-304.(ZHANG C S, SUN J G, OUYANG D T. A self-adaptive discrete particle swarm optimization algorithm[J]. Acta Electronica Sinica, 2009, 37(2):299-304.)
[21] LATOMBE J C. Robot Motion Planning[M]. Berlin:Kluwer Academic Publishers, 1991:19-20.

固定翼无人机在线航迹规划方法

On-line path planning method of fixed-wing unmanned aerial vehicle

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