Volume 50 Issue 5
May  2024
Turn off MathJax
Article Contents
WANG F Y,MENG X Y,ZHANG H K. UAV three-dimensional path planning based on ε-level bat algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(5):1593-1603 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0502
Citation: WANG F Y,MENG X Y,ZHANG H K. UAV three-dimensional path planning based on ε-level bat algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(5):1593-1603 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0502

UAV three-dimensional path planning based on ε-level bat algorithm

doi: 10.13700/j.bh.1001-5965.2022.0502
More Information
  • Corresponding author: E-mail:mengxy@bit.edu.cn
  • Received Date: 20 Jun 2022
  • Accepted Date: 12 Aug 2022
  • Available Online: 03 Feb 2023
  • Publish Date: 18 Jan 2023
  • To address the problem of complex terrain environment and various threats and constraints, this article proposes a path planning algorithm for UAV based on ε-level improved bat algorithm. First, according to the drone target function and constraints, a three-dimensional path planning model of the UAV is established. Second, in response to the precocious phenomenon in handling the high-dimensional constraints problem of the bat algorithm, the adaptive weight coefficient and iteration threshold are designed to balance the exploration and exploitation capabilities of bat algorithms. Furthermore, by integrating an ε-level comparative strategy, the algorithm's capability to handle issues of non-convex and non-linear constraints is enhanced. Additionally, a three-dimensional Dubins curve with variable turning radius is designed to smooth the path and solve the problem of penetrating the terrain of the two trails. Through simulation experiments and compared with BA, PSO, ε-PSO and ε-DE, the algorithm proposed in this paper shows superior performance in terms of exploitation ability, stability and success rate.

     

  • loading
  • [1]
    SANTOSO F, GARRATT M, ANAVATTI S. State-of-the-art intelligent flight control systems in unmanned aerial vehicles[J]. IEEE Transactions on Automation Science and Engineering, 2018, 15(2): 613-627. doi: 10.1109/TASE.2017.2651109
    [2]
    ZHAO Y J, ZHENG Z, LIU Y. Survey on computational-intelligence-based UAV path planning[J]. Knowledge-Based Systems, 2018, 158: 54-64. doi: 10.1016/j.knosys.2018.05.033
    [3]
    LIU H, ZHANG N, LI Q. UAV path planning based on an improved ant colony algorithm[C]//Proceedings of 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS). Piscataway: IEEE Press, 2021: 357-360.
    [4]
    陈侠, 刘奎武, 毛海亮. 基于APF-RRT算法的无人机航迹规划[J]. 电光与控制, 2022, 29(5): 17-22.

    CHEN X, LIU K W, MAO H L. The path planning of UAV based on APF-RRT algorithm[J]. Electronics Optics & Control, 2022, 29(5): 17-22(in Chinese).
    [5]
    赵红超, 周洪庆, 王书湖. 无人机三维航迹规划的量子粒子群优化算法[J]. 航天控制, 2021, 39(1): 40-45.

    ZHAO H C, ZHOU H Q, WANG S H. Quantum particle swarm optimization algorithm of three-dimensional path planning of unmanned aerial vehicle[J]. Aerospace Control, 2021, 39(1): 40-45(in Chinese).
    [6]
    SHIVGAN R, DONG Z. Energy-efficient drone coverage path planning using genetic algorithm[C]//Proceedings of 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR). Piscataway: IEEE Press, 2020: 1-6.
    [7]
    汤安迪, 韩统, 徐登武, 等. 混沌多精英鲸鱼优化算法[J]. 北京航空航天大学学报, 2021, 47(7): 1481-1494.

    TANG A D, HAN T, XU D W, et al. Chaotic multi-leader whale optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(7): 1481-1494(in Chinese).
    [8]
    GHAMBARI S, LEPAGNOT J, JOURDAN L, et al. UAV path planning in the presence of static and dynamic obstacles[C]//Proceedings of 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway: IEEE Press, 2020, 465-472.
    [9]
    高阳阳, 余敏建, 韩其松, 等. 基于改进共生生物搜索算法的空战机动决策[J]. 北京航空航天大学学报, 2019, 45(3): 429-436.

    GAO Y Y, YU M J , HAN Q S, et al. Air combat maneuver decision-making based on improved symbiotic organisms search algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 429-436(in Chinese).
    [10]
    JAMSHIDI V, NEKOUKAR V, REFAN M H. Real time UAV path planning by parallel grey wolf optimization with align coefficient on CAN bus[J]. Cluster Computing-The Journal of Networks Software Tools and Applications, 2021, 24(3): 2495-2509.
    [11]
    YANG X. A new metaheuristic bat-inspired algorithm[C]//Proceedings of Nature Inspired Cooperative Strategies for Optimization(NICSO 2010). Berlin: Springer, 2010: 65-74.
    [12]
    GANDOMI A, YANG X. Chaotic bat algorithm[J]. Journal of Computational Science, 2014, 5(2): 224-232. doi: 10.1016/j.jocs.2013.10.002
    [13]
    LIU Q, WU L, XIAO W S, et al. A novel hybrid bat algorithm for solving continuous optimization problems[J]. Applied Soft Computing, 2018, 73: 67-82. doi: 10.1016/j.asoc.2018.08.012
    [14]
    RAUF H, MALIK S, SHOAIB U, et al. Adaptive inertia weight bat algorithm with sugeno-function fuzzy search[J]. Applied Soft Computing, 2020, 90: 106159. doi: 10.1016/j.asoc.2020.106159
    [15]
    LYU S L, LI Z, HUANG Y L, et al. Improved self-adaptive bat algorithm with step-control and mutation mechanisms[J]. Journal of Computational Science, 2019, 30: 65-78. doi: 10.1016/j.jocs.2018.11.002
    [16]
    YANG X, GANDOMI A. Bat algorithm: a novel approach for global engineering optimization[J]. Engineering Computations, 2012, 29(5): 464-483. doi: 10.1108/02644401211235834
    [17]
    OSABA E, YANG X, DIAZ F, et al. An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems[J]. Engineering Applications of Artificial Intelligence, 2016, 48: 59-71. doi: 10.1016/j.engappai.2015.10.006
    [18]
    TALBI N. Design of fuzzy controller rule base using bat algorithm[J]. Energy Procedia, 2019, 162: 241-250. doi: 10.1016/j.egypro.2019.04.026
    [19]
    HASANÇEBI O, TEKE T, PEKCAN O. A bat-inspired algorithm for structural optimization[J]. Computers & Structures, 2013, 128: 77-90.
    [20]
    WANG G G, CHU H C, SEYEDALI M. Three-dimensional path planning for UCAV using an improved bat algorithm[J]. Aerospace Science and Technology, 2016, 49: 231-238. doi: 10.1016/j.ast.2015.11.040
    [21]
    HOLUB J, FOO J, KILIVARAPU V, et al. Three dimensional multi-objective UAV path planning using digital pheromone particle swarm optimization[C]//Proceedings of 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston: AIAA, 2012: 1525.

    HOLUB J, FOO J, KILIVARAPU V, et al. Three dimensional multi-objective UAV path planning using digital pheromone particle swarm optimization[C]//Proceedings of 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston: AIAA, 2012: 1525.
    [22]
    DADGAR M, JAFARI S, HAMZEH A. A PSO-based multi-robot cooperation method for target searching in unknown environments[J]. Neurocomputing, 2016, 177: 62-74. doi: 10.1016/j.neucom.2015.11.007
    [23]
    TAKAHAMA T, SAKAI S. Constrained optimization by the ε constrained differential evolution with an archive and gradient-based mutation[C]//Proceedings of IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press, 2010: 1-9.
    [24]
    TAKAHAMA T, SAKAI S. Solving constrained optimization problems by the ε constrained particle swarm optimizer with adaptive velocity limit control[C]//Proceedings of IEEE Conference on Cybernetics & Intelligent Systems. Piscataway: IEEE Press, 2006: 1-7.

    TAKAHAMA T, SAKAI S. Solving constrained optimization problems by the ε constrained particle swarm optimizer with adaptive velocity limit control[C]//Proceedings of IEEE Conference on Cybernetics & Intelligent Systems. Piscataway: IEEE Press, 2006: 1-7.
    [25]
    WANG L, LI P. An effective differential evolution with level comparison for constrained engineering design[J]. Structural and Multidisciplinary Optimization, 2010, 41(6): 947-963. doi: 10.1007/s00158-009-0454-5
    [26]
    ALLAIRE FCJ, TARBOUCHI M, LABONTÉ G, et al. Real-time UAV path-terrain collision evaluation on FPGA[C]//Proceedings of 2018 4th International Conference on Optimization and Applications (ICOA). Piscataway: IEEE Press, 2018: 1-5.

    ALLAIRE FCJ, TARBOUCHI M, LABONTÉ G, et al. Real-time UAV path-terrain collision evaluation on FPGA[C]//Proceedings of 2018 4th International Conference on Optimization and Applications (ICOA). Piscataway: IEEE Press, 2018: 1-5.
    [27]
    SHANMUGAVEL M, TSOURDOS A, ZBIKOWSKI R, et al. Path planning of multiple UAVs using dubins sets[C]//Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston: AIAA, 2005: 5827.

    SHANMUGAVEL M, TSOURDOS A, ZBIKOWSKI R, et al. Path planning of multiple UAVs using dubins sets[C]//Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston: AIAA, 2005: 5827.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(2)

    Article Metrics

    Article views(703) PDF downloads(14) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return