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考虑先验信息的多机器人重点区域协同覆盖

段安娜 周锐 邸斌

段安娜,周锐,邸斌. 考虑先验信息的多机器人重点区域协同覆盖[J]. 北京航空航天大学学报,2023,49(6):1479-1486 doi: 10.13700/j.bh.1001-5965.2021.0435
引用本文: 段安娜,周锐,邸斌. 考虑先验信息的多机器人重点区域协同覆盖[J]. 北京航空航天大学学报,2023,49(6):1479-1486 doi: 10.13700/j.bh.1001-5965.2021.0435
DUAN A N,ZHOU R,DI B. Multi-robot cooperative coverage of key regions considering prior information[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1479-1486 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0435
Citation: DUAN A N,ZHOU R,DI B. Multi-robot cooperative coverage of key regions considering prior information[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(6):1479-1486 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0435

考虑先验信息的多机器人重点区域协同覆盖

doi: 10.13700/j.bh.1001-5965.2021.0435
基金项目: 国家自然科学基金(62003365,61773031);中国电子科技集团公司航天信息应用技术重点实验室开放基金(SCX20629T007)
详细信息
    通讯作者:

    E-mail:dibin@buaa.edu.cn

  • 中图分类号: TP242.6

Multi-robot cooperative coverage of key regions considering prior information

Funds: National Natural Science Foundation of China (62003365,61773031); CETC Key Laboratory of Aerospace Information Applications Foundation (SCX20629T007)
More Information
  • 摘要:

    针对复杂环境下的多移动机器人对重点目标区域协同持续监视覆盖问题,假定在目标区域内分布着若干障碍及固定观测点,已获得固定观测点处所关心的目标参量历史测量数据,考虑为机器人群规划一组监视路径,以实现对重点目标区域高覆盖率、高频率的监视覆盖。建立多机器人协同持续监视问题的数学模型;基于小脑模型神经网络(CMAC)对区域内固定观测点的测量数据进行学习泛化以获得区域内目标参量估计;利用基于传感器配置-路径框架划分的路径规划组合策略以求得各机器人优化路径。仿真实验验证了模型和求解方法的有效性。

     

  • 图 1  复杂目标区域及固定观测点示意图

    Figure 1.  Schematic diagram of complex target area and fixed observation point

    图 2  考虑先验信息的多机器人重点区域协同覆盖问题求解策略示意图

    Figure 2.  Schematic diagram of solution strategy for multi-robot cooperative coverage of key regions with prior information

    图 3  CMAC多个交叠覆盖划分示意图[11]

    Figure 3.  Schematic diagram of CMAC with multiple overlapping coverage partitions[11]

    图 4  虚拟传感器配置及路径框架环绕示意图

    Figure 4.  Schematic diagram of virtual sensor configuration and path framework

    图 5  目标区域环境及固定观测点示意图

    Figure 5.  Schematic diagram of target area environment and fixed observation point

    图 6  区域内目标参量信息

    Figure 6.  Estimated value of target parameters within area

    图 7  虚拟传感器配置情况

    Figure 7.  Configuration of virtual sensors

    图 8  求解旅行商问题所得路径框架

    Figure 8.  Path framework obtained from solution to traveling salesman problem

    图 10  不同机器人投放数量时各机器人监视覆盖优化路径

    Figure 10.  Optimized monitoring coverage path of each robot with different deployment quantities

    图 9  不同固定频率f0对路径规划的影响

    Figure 9.  Effect of different fixed frequencies f0 on path planning

    表  1  不同固定频率f0下各机器人监视覆盖频率

    Table  1.   Monitoring coverage frequency of each robot with different fixed frequencies Hz

    机器人编号f0=0.0250f0=0.0167f0=0.0125
    10.02500.01550.0114
    20.02500.00660.0066
    30.01600.01600.0122
    4 0.01300.01740.0130
    下载: 导出CSV

    表  2  不同机器人投放数量持续搜索时各机器人监视覆盖频率

    Table  2.   Monitoring coverage frequency of each robot with different number of robots Hz

    机器人编号数量为4数量为6数量为8数量为10
    10.01550.01550.01490.0152
    20.00660.01550.01550.0155
    30.01600.01660.01550.0155
    40.01740.00760.01660.0166
    50.01600.01740.0122
    60.01300.01630.0091
    70.00530.0124
    80.01740.0174
    90.0117
    100.0164
    下载: 导出CSV
  • [1] CHOSET H M, LYNCH K M, HUTCHINSON S, et al. Principles of robot motion: Theory, algorithms, and implementation[M]. Cambridge: MIT Press, 2005: 162-168.
    [2] CHOSET H, ACAR E, RIZZI A A, et al. Exact cellular decompositions in terms of critical points of Morse functions[C]//Proceedings 2002 IEEE International Conference on Robotics and Automation. Symposia Proceedings. Piscataway: IEEE Press, 2002: 2270-2277.
    [3] ACAR E U, CHOSET H, RIZZI A A, et al. Morse decompositions for coverage tasks[J]. The International Journal of Robotics Research, 2002, 21(4): 331-344. doi: 10.1177/027836402320556359
    [4] YAZICI A, KIRLIK G, PARLAKTUNA O, et al. A dynamic path planning approach for multirobot sensor-based coverage considering energy constraints[J]. IEEE Transactions on Cybernetics, 2014, 44(3): 305-314. doi: 10.1109/TCYB.2013.2253605
    [5] GABRIELY Y, RIMON E. Spiral-STC: An on-line coverage algorithm of grid environments by a mobile robot[C]//Proceedings 2002 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2002: 954-960.
    [6] GAO C Q, KOU Y X, LI Z W, et al. Optimal multirobot coverage path planning: Ideal-shaped spanning tree[J]. Mathematical Problems in Engineering, 2018, 2018: 1-10.
    [7] KAPOUTSIS A C, CHATZICHRISTOFIS S A, KOSMATOPOULOS E B. DARP: Divide areas algorithm for optimal multi-robot coverage path planning[J]. Journal of Intelligent & Robotic Systems, 2017, 86(3): 663-680.
    [8] ZHOU X, WANG H M, DING B. How many robots are enough: A multi-objective genetic algorithm for the single-objective time-limited complete coverage problem[C]//2018 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2018: 2380-2387.
    [9] KRAUSE A, SINGH A, GUESTRIN C. Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies[J]. Journal of Machine Learning Research, 2008, 9(2): 235-284.
    [10] MOHAJERI K, PISHEHVAR G, SEIFI M. CMAC neural networks structures[C]//2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Piscataway: IEEE Press, 2010: 39-45.
    [11] MILLER W T, GLANZ F H, KRAFT L G. CMAC: An associative neural network alternative to backpropagation[J]. Proceedings of the IEEE, 1990, 78(10): 1561-1567. doi: 10.1109/5.58338
    [12] SUTTON R S. Generalization in reinforcement learning: Successful examples using sparse coarse coding[C]//Proceedings of the 8th International Conference on Neural Information Processing Systems. New York: ACM, 1995: 1038–1044.
    [13] GENG Z J, MCCULLOUGH C L. Missile control using fuzzy cerebellar model arithmetic computer neural networks[J]. Journal of Guidance, Control, and Dynamics, 1997, 20(3): 557-565. doi: 10.2514/2.4077
    [14] FANG W B, CHAO F, YANG L Z, et al. A recurrent emotional CMAC neural network controller for vision-based mobile robots[J]. Neurocomputing, 2019, 334(C): 227-238.
    [15] SUN G B, ZHOU R, DI B, et al. A novel cooperative path planning for multi-robot persistent coverage with obstacles and coverage period constraints[J]. Sensors, 2019, 19(9): 1994. doi: 10.3390/s19091994
    [16] SUTTON R, BARTO A著, 俞凯等译. 强化学习[M]. 北京: 电子工业出版社, 2019: 198-218.

    SUTTON R, BARTO A, Translated by Yu K et al. Reinforcement learning[M]. Beijing: Publishing House of Electronics Industry, 2019: 198-218(in Chinese).
    [17] WANG Y C, TSENG Y C. Distributed deployment schemes for mobile wireless sensor networks to ensure multilevel coverage[J]. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(9): 1280-1294. doi: 10.1109/TPDS.2007.70808
    [18] CHANG C Y, CHANG C T, CHEN Y C, et al. Obstacle-resistant deployment algorithms for wireless sensor networks[J]. IEEE Transactions on Vehicular Technology, 2009, 58(6): 2925-2941. doi: 10.1109/TVT.2008.2010619
    [19] LEE J Y, SEOK J H, LEE J J. Multiobjective optimization approach for sensor arrangement in a complex indoor environment[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2012, 42(2): 174-186. doi: 10.1109/TSMCC.2010.2103310
    [20] SEOK J H, LEE J Y, KIM W, et al. A bipopulation-based evolutionary algorithm for solving full area coverage problems[J]. IEEE Sensors Journal, 2013, 13(12): 4796-4807. doi: 10.1109/JSEN.2013.2274693
    [21] GHOSH A, DAS S K. Coverage and connectivity issues in wireless sensor networks: A survey[J]. Pervasive and Mobile Computing, 2008, 4(3): 303-334. doi: 10.1016/j.pmcj.2008.02.001
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出版历程
  • 收稿日期:  2021-08-02
  • 录用日期:  2022-03-04
  • 网络出版日期:  2022-03-22
  • 整期出版日期:  2023-06-30

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