Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm

被引:1
|
作者
Sun, Cheng [1 ]
Yao, Yuwen [1 ]
Zheng, Enhui [1 ]
机构
[1] China Jiliang Univ, Sch Mech & Elect Engn, Hangzhou 310018, Peoples R China
关键词
multi-UAV task assignment; auction algorithm; task rationality review;
D O I
10.3390/drones8090422
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Multi-unmanned Aerial Vehicle Cooperative Task Allocation Algorithm Based on Improved Distributed Cooperative Auction
    Bao, Kanghua
    Yi, Shuang
    Zhang, Hongjiang
    He, Pengsheng
    PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022, 2023, 1010 : 1535 - 1543
  • [22] AN ADAPTIVE CONTROL ALGORITHM OF TETHERED UNMANNED AERIAL VEHICLE
    Huang, Xiang
    Gu, Xu
    Du, Biao
    Huo, Danjiang
    MECHATRONIC SYSTEMS AND CONTROL, 2024, 52 (01): : 11 - 17
  • [23] Task Allocation Method of Manned/Unmanned Aerial Vehicle Formation Based on Extended CNP
    Liu Yuefeng
    Zou Jie
    Sun Houjun
    2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), 2016, : 1975 - 1979
  • [24] Sampling-based methods for factored task and motion planning
    Garrett, Caelan Reed
    Lozano-Perez, Tomas
    Kaelbling, Leslie Pack
    INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2018, 37 (13-14): : 1796 - 1825
  • [25] Compression Based Distributed Dynamic Task Assignment Algorithms for Heterogeneous Multiple Unmanned Aerial Vehicles
    Wang, Li
    Guo, Qiao
    2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE ROBIO 2017), 2017, : 2401 - 2406
  • [26] Effect of haptic feedback in a trajectory following task with an unmanned aerial vehicle
    Lam, TM
    Boschloo, HW
    Mulder, M
    van Paassen, MM
    van der Helm, FCT
    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7, 2004, : 2500 - 2506
  • [27] A* algorithm based on adaptive expansion convolution for unmanned aerial vehicle path planning
    Xu, Yu
    Li, Yang
    Tai, Yubo
    Lu, Xiaohan
    Jia, Yaodong
    Wang, Yifan
    INTELLIGENT SERVICE ROBOTICS, 2024, 17 (03) : 521 - 531
  • [28] The Collaborative Power Inspection Task Allocation Method of "Unmanned Aerial Vehicle and Operating Vehicle"
    Zheng, Huang
    Hongxing, Wang
    Tianpei, Zhang
    Bin, Yu
    IEEE ACCESS, 2021, 9 : 62926 - 62934
  • [29] Multiple Task Planning Based on TS Algorithm for Multiple Heterogeneous Unmanned Aerial Vehicles
    Wang Zheng
    Liu Qiaoqiao
    Tao Hongtao
    Li Jianxun
    2014 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC), 2014, : 630 - 635
  • [30] Distributed Cooperative Search Algorithm With Task Assignment and Receding Horizon Predictive Control for Multiple Unmanned Aerial Vehicles
    Hou, Kun
    Yang, Yajun
    Yang, Xuerong
    Lai, Jiazhe
    IEEE ACCESS, 2021, 9 : 6122 - 6136