[1]秦冬燕,闫晓辉,邵桂伟,等.基于事件触发灰狼优化算法的四旋翼无人机三维航迹规划[J].智能系统学报,2025,20(3):699-706.[doi:10.11992/tis.202406013]
QIN Dongyan,YAN Xiaohui,SHAO Guiwei,et al.Event-triggered gray wolf optimization for quadrotor unmanned aerial vehicle three-dimensional trajectory planning[J].CAAI Transactions on Intelligent Systems,2025,20(3):699-706.[doi:10.11992/tis.202406013]
点击复制
QIN Dongyan,YAN Xiaohui,SHAO Guiwei,et al.Event-triggered gray wolf optimization for quadrotor unmanned aerial vehicle three-dimensional trajectory planning[J].CAAI Transactions on Intelligent Systems,2025,20(3):699-706.[doi:10.11992/tis.202406013]
基于事件触发灰狼优化算法的四旋翼无人机三维航迹规划
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第3期
页码:
699-706
栏目:
学术论文—智能系统
出版日期:
2025-05-05
- Title:
- Event-triggered gray wolf optimization for quadrotor unmanned aerial vehicle three-dimensional trajectory planning
- Keywords:
- improved GWO algorithm; event-triggered; 3D path planning; spherical vector; adaptive weight; nonlinear convergence factor; velocity pausing; quadrotor unmanned aerial vehicle
- 分类号:
- TP242
- 摘要:
- 针对复杂环境下四旋翼无人机三维航迹规划问题，提出了一种改进的事件触发灰狼优化算法(event triggered grey wolf optimization，ETGWO)。引入球面矢量刻画飞行路径的生成，通过减少搜索空间提升搜索能力；设计自适应权重动态调整飞行航迹成本适应度函数，以提高航迹规划效率和准确性；在灰狼优化算法(grey wolf optimization，GWO)基础上，选择使用改进的非线性收敛因子，提升算法的鲁棒性；为了更好地平衡算法的全局搜索和局部搜索能力，通过引入基于事件触发机制的灰狼个体位置更新速度来改进GWO算法的位置更新策略。仿真对比实验表明，所提出ETGWO算法在四旋翼无人机(quadrotor unmanned aerial vehicles, QUAV)飞行航迹规划方面具有更优越的性能。
- Abstract:
- An improved event-triggered gray wolf optimization algorithm (ETGWO) is proposed for the 3D path planning of quadrotor unmanned aerial vehicles in complex environments. First, the search space and search capabilities are reduced and improved, respectively, by introducing spherical vectors to characterize flight path generation. Second, the adaptive weights are designed to dynamically adjust the fitness function of the flight trajectory cost, thereby improving the efficiency and accuracy of path planning. Based on the gray wolf optimization (GWO) algorithm, a nonlinear convergence factor is selected to enhance the robustness of the algorithm. Additionally, to better balance the global and local search capabilities, the position update strategy of the gray wolf individuals is developed based on the update velocity, which is integrated with the event-triggered mechanism. Finally, simulation and comparative experiments are performed to confirm the superior planning performance of ETGWO compared with those of other algorithms.
- 参考文献/References:
-
[1] EL HAMMOUTI H, HAMZA D, SHIHADA B, et al. The optimal and the greedy: drone association and positioning schemes for Internet of UAVs[J]. IEEE internet of things journal, 2021, 8(18): 14066-14079.
[2] MASROOR R, NAEEM M, EJAZ W. Efficient deployment of UAVs for disaster management: a multi-criterion optimization approach[J]. Computer communications, 2021, 177: 185-194.
[3] WU Yu, WU Shaobo, HU Xinting. Cooperative path planning of UAVs & UGVs for a persistent surveillance task in urban environments[J]. IEEE internet of things journal, 2021, 8(6): 4906-4919.
[4] CUI Guozeng, XU Hui, YU Jinpeng, et al. Fixed-time distributed adaptive attitude control for multiple QUAVs with quantized input[J]. Applied mathematics and computation, 2023, 449: 127933.
[5] KHALID A, MUSHTAQ Z, ARIF S, et al. Control schemes for quadrotor UAV: taxonomy and survey[J]. ACM computing surveys, 2024, 56(5): 1-32.
[6] JIANG Wei, LYU Yongxi, LI Yongfeng, et al. UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer[J]. Aerospace science and technology, 2022, 121: 107314.
[7] AN V, QU Zhihua, CROSBY F, et al. A triangulation-based coverage path planning[J]. IEEE transactions on systems, man, and cybernetics: systems, 2020, 50(6): 2157-2169.
[8] RASEKHIPOUR Y, KHAJEPOUR A, CHEN S K, et al. A potential field-based model predictive path-planning controller for autonomous road vehicles[J]. IEEE transactions on intelligent transportation systems, 2017, 18(5): 1255-1267.
[9] KARAMAN S, WALTER M R, PEREZ A, et al. Anytime motion planning using the RRT[C]//2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 1478-1483.
[10] 郭娜, 李彩虹, 王迪, 等. 结合预测和模糊控制的移动机器人路径规划[J]. 计算机工程与应用, 2020, 56(8): 104-109.
GUO Na, LI Caihong, WANG Di, et al. Path planning of mobile robot based on prediction and fuzzy control[J]. Computer engineering and applications, 2020, 56(8): 104-109.
[11] SELIM S Z, ALSULTAN K. A simulated annealing algorithm for the clustering problem[J]. Pattern recognition, 1991, 24(10): 1003-1008.
[12] DENG Yong, CHEN Yuxin, ZHANG Yajuan, et al. Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment[J]. Applied soft computing, 2012, 12(3): 1231-1237.
[13] DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische mathematik, 1959, 1(1): 269-271.
[14] SHI Kun, WU Zhengtian, JIANG Baoping, et al. Dynamic path planning of mobile robot based on improved simulated annealing algorithm[J]. Journal of the Franklin institute, 2023, 360(6): 4378-4398.
[15] AGGARWAL S, KUMAR N. Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges[J]. Computer communications, 2020, 149: 270-299.
[16] 杨旭, 王锐, 张涛. 面向无人机集群路径规划的智能优化算法综述[J]. 控制理论与应用, 2020, 37(11): 2291-2302.
YANG Xu, WANG Rui, ZHANG Tao. Review of unmanned aerial vehicle swarm path planning based on intelligent optimization[J]. Control theory & applications, 2020, 37(11): 2291-2302.
[17] TANG Jun, LIU Gang, PAN Qingtao. A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends[J]. IEEE/CAA journal of automatica sinica, 2021, 8(10): 1627-1643.
[18] 辛建霖, 左家亮, 岳龙飞, 等. 基于改进启发式蚁群算法的无人机自主航迹规划[J]. 航空工程进展, 2022, 13(1): 60-67.
XIN Jianlin, ZUO Jialiang, YUE Longfei, et al. Autonomous path planning for unmanned aerial vehicle(UAV) based on improved heuristic ant colony algorithm[J]. Advances in aeronautical science and engineering, 2022, 13(1): 60-67.
[19] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN’95 - International Conference on Neural Networks. Perth: IEEE, 1995: 1942-1948.
[20] 张毅, 杨光辉, 花远红. 基于改进人工鱼群算法的机器人路径规划[J]. 控制工程, 2020, 27(7): 1157-1163.
ZHANG Yi, YANG Guanghui, HUA Yuanhong. Robot path planning based on improved artificial fish swarm algorithm[J]. Control engineering of China, 2020, 27(7): 1157-1163.
[21] 魏永超, 邓岚, 李涛, 等. 采用改进细菌觅食优化算法的无人机航迹规划[J]. 电讯技术, 2021, 61(5): 560-566.
WEI Yongchao, DENG Lan, LI Tao, et al. UAV track planning with improved bacterial foraging optimization algorithm[J]. Telecommunication engineering, 2021, 61(5): 560-566.
[22] HEWAWASAM H S, IBRAHIM M Y, APPUHAMILLAGE G K. Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments[J]. IEEE open journal of the industrial electronics society, 2022, 3: 353-365.
[23] PHUNG M D, HA Q P. Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization[J]. Applied soft computing, 2021, 107: 107376.
[24] XU Guiping, CUI Quanlong, SHI Xiaohu, et al. Particle swarm optimization based on dimensional learning strategy[J]. Swarm and evolutionary computation, 2019, 45: 33-51.
[25] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[26] 王永琦, 江潇潇. 基于混合灰狼算法的机器人路径规划[J]. 计算机工程与科学, 2020, 42(7): 1294-1301.
WANG Yongqi, JIANG Xiaoxiao. Robot path planning using a hybrid grey wolf optimization algorithm[J]. Computer engineering & science, 2020, 42(7): 1294-1301.
[27] 曹梦龙, 赵文彬, 陈志强. 融合粒子群算法与改进灰狼算法的机器人路径规划[J]. 系统仿真学报, 2023, 35(8): 1768-1775.
CAO Menglong, ZHAO Wenbin, CHEN Zhiqiang. Robot path planning by fusing particle swarm algorithm and improved grey wolf algorithm[J]. Journal of system simulation, 2023, 35(8): 1768-1775.
[28] YU Zhenhua, SI Zhijie, LI Xiaobo, et al. A novel hybrid particle swarm optimization algorithm for path planning of UAVs[J]. IEEE internet of things journal, 2022, 9(22): 22547-22558.
[29] YAN Ziwei, HAN Liang, LI Xiaoduo, et al. Event-Triggered formation control for time-delayed discrete-time multi-agent system applied to multi-UAV formation flying[J]. Journal of the Franklin Institute, 2023, 360(5): 3677-3699.
[30] 王海群, 邓金铭, 张怡, 等. 基于改进混合灰狼优化算法的无人机三维路径规划[J]. 无线电工程, 2024, 54(4): 918-927.
WANG Haiqun, DENG Jinming, ZHANG Yi, et al. UAV 3D path planning based on improved hybrid grey wolf optimization algorithm[J]. Radio engineering, 2024, 54(4): 918-927.
- 相似文献/References:
-
[1]夏倩倩,刘开恩,纪志坚.基于二阶邻居事件触发多智能体系统的一致性[J].智能系统学报,2017,12(6):833.[doi:10.11992/tis.201702008]
XIA Qianqian,LIU Kaien,JI Zhijian.Event-triggered consensus of multi-agent systems based on second-order neighbors[J].CAAI Transactions on Intelligent Systems,2017,12():833.[doi:10.11992/tis.201702008]
[2]郝勇,贾登辉,李晨洋,等.基于事件触发的预设时间航天器轨迹跟踪控制[J].智能系统学报,2024,19(3):661.[doi:10.11992/tis.202207017]
HAO Yong,JIA Denghui,LI Chenyang,et al.Event trigger-based prescribed-time tracking control of spacecraft[J].CAAI Transactions on Intelligent Systems,2024,19():661.[doi:10.11992/tis.202207017]
备注/Memo
收稿日期:2024-6-11。
基金项目:国家自然科学基金项目(61903118)；安徽省自然科学基金项目(1908085QF290)；合肥学院人才科研基金项目(20RC27).
作者简介:秦冬燕，硕士研究生，主要研究方向为四旋翼无人机路径规划。E-mail：2671812444@qq.com。;闫晓辉，教授，主要研究方向为非线性系统控制、无人系统智能控制、轨迹规划。发表学术论文 30 余篇。E-mail：xhyan@hfuu.edu.cn。;邵桂伟，副教授，主要研究方向为算法、强化学习与优化控制。E-mail：shaoguiwei@hfuu.edu.cn。
通讯作者:闫晓辉. E-mail：xhyan@hfuu.edu.cn
更新日期/Last Update:
1900-01-01