多机器人组合最大覆盖面积寻优及预测方法

doi:10.16182/j.issn1004731x.joss.20-0630

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (1): 86-92.doi: 10.16182/j.issn1004731x.joss.20-0630

多机器人组合最大覆盖面积寻优及预测方法

王语童¹, 马世伟¹, 杨元睿², 陈超宇²

1.上海大学机电工程与自动化学院,上海 200444;
2.新加坡国立大学机械工程系,新加坡 117576

收稿日期:2020-08-26 修回日期:2020-09-16 出版日期:2022-01-18 发布日期:2022-01-14
通讯作者: 马世伟(1965-),男,博士,教授,研究方向为信号处理、图像处理和模式识别等。E-mail：masw@shu.edu.cn
作者简介:王语童(1998-)，女，硕士生，研究方向为多机器人深度强化学习。E-mail：13508894496@163.com
基金资助:
新疆兵团重大项目子项目(2018AA008-04)

Optimization and Prediction for Multi-robot Combination Maximum Coverage Area

Wang Yutong¹, Ma Shiwei¹, Yang Yuanrui², Chen Chaoyu²

1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
2. Department of Mechanical Engineering, National University of Singapore, Singapore 117576, China

Received:2020-08-26 Revised:2020-09-16 Online:2022-01-18 Published:2022-01-14

摘要/Abstract

摘要： 针对多机器人协同情况下最大覆盖面积的最优控制问题,提出了一种遵循叠加原则和强度径向衰减圆盘模型的多机器人组合有效覆盖面积估计、寻优、预测方法。使用蒙特卡罗法对机器人组合的有效覆盖面积值进行估算;使用多种群遗传算法得出组合的最大有效覆盖面积;使用支持向量回归机预测机器人个数与最大有效覆盖面积之间的关系。针对寻优以及预测结果进行了仿真实验,结果表明：在目标函数复杂和训练样本数少的情况下该方法具有良好的寻优和预测性能。

关键词: 多机器人协同, 覆盖面积, 多种群遗传算法, 径向衰减圆盘模型

Abstract: Aiming at the optimal control of the multi-robot combination maximum coverage area, based on the intensity radial attenuation disc model and following the superposition principle, a method for estimating, optimizing and predicting the effective coverage area of the multi-robot combination is proposed. The Monte Carlo method is used to estimate the effective coverage area of the robot combination, and the multiple population genetic algorithm is used to obtain the maximum effective coverage area of the combination, and the support vector machine regression is used to predict the relationship between the number of robots and the maximum effective coverage area. Simulation experiments are carried out for the optimization and prediction results. The results show that the method has good optimization and prediction performance when the target function is complex, and the number of training samples is limited.

Key words: multi-robot collaboration, coverage area, multi-group genetic algorithm, radial attenuation disk model

中图分类号:

TP391.4

王语童, 马世伟, 杨元睿, 陈超宇. 多机器人组合最大覆盖面积寻优及预测方法[J]. 系统仿真学报, 2022, 34(1): 86-92.

Wang Yutong, Ma Shiwei, Yang Yuanrui, Chen Chaoyu. Optimization and Prediction for Multi-robot Combination Maximum Coverage Area[J]. Journal of System Simulation, 2022, 34(1): 86-92.

参考文献

[1] 郭超, 杨宇轩, 胡荣磊, 等. 基于粒子群算法的WSN覆盖优化[J]. 计算机应用研究, 2020, 37(4): 5.
Guo Chao, Yang Yuxuan, Hu Ronglei, et al.WSN Coverage Optimization Based on Particle Swarm Optimization[J]. Application Research of Computers, 2020, 37(4): 5.
[2] 王振东, 刘燔桃, 胡中栋, 等. 改进差分进化算法下的无线传感器网络覆盖优化[J]. 小型微型计算机系统, 2020, 41(5): 6.
Wang Zhendong, Liu Fantao, Hu Zhongdong, et al.Coverage Optimization of Wireless Sensor Networks Based on Improved Differential Evolution Algorithm[J]. Journal of Chinese Computer Systems, 2020, 41(5): 6.
[3] 齐薇, 虞慧群, 范贵生, 等. 基于自适应粒子群的WSN覆盖优化[J]. 计算机科学, 2020, 47(7): 243-249.
Qi Wei, Yu Huiqun, Fan Guisheng, et al.WSN Coverage Optimization Based on Adaptive Particle Swarm Optimization[J]. Computer Science, 2020, 47(7): 243-249.
[4] Liao Z F, Wang J X, Zhang S G, et al.Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Metworks[J]. IEEE Transactions on Parallel and Distributed Systems (S1045-9219), 2015, 26(7): 1971-1983.
[5] Khampeerpat T, Jaikaeo C.Mobile Sensor Relocation for Nonuniform and Dynamic Coverage Requirements[J]. The Institute of Electronics, Information and Communication Engineers (S0913-5693), 2017, 100(3): 520-530.
[6] Ganganath N, Cheng C T, Tse C K.Distributed Anti?ocking Algorithms for Dynamic Coverage of Mobile Sensor Networks[J]. IEEE Transactions on Industrial Informatics (S1551-3203), 2016, 12(5): 1795-1805.
[7] Chand S, Kumar B.Genetic Algorithm-Based Meta-heuristic for Target Coverage Problem[J]. IET Wireless Sensor Systems (S2043-6394), 2018, 8(4): 170-175.
[8] Liang C K, Lin Y H.A Coverage Optimization Strategy for Mobile Wireless Sensor Networks Based on Genetic Algorithm[C]//IEEE International Conference on Applied System Invention (ICASI). Chiba: IEEE, 2018. 1272-1275.
[9] Binh H T T, Hanh N T, Dey N. Improved Cuckoo Search and Chaotic Flow Pollination Optimization Algorithm for Maximizing Area Coverage in Wireless Sensor Networks[J]. Neural Computing and Applications (S1433-3058), 2018, 30(7): 2305-2317.
[10] Cochran J K, Horng S M, Fowler J W.A Multi-Population Genetic Algorithm to Solve Multi-Objective Scheduling Problems for Parallel Machines[J]. Computers & Operations Research (S0305-0548), 2003, 30(7): 1087-1102.
[11] Drucker H, Burges C J C, Kaufman L, et al. Support vector regression machines[J]. Advances in neural information processing systems(S1049-5258), 1997, 28(7): 779-784.
[12] Yang Y R, Chen C Y P. Effective Coverage of Two-Robot Team with Radial Attenuation Models[J]. Journal of Intelligent and Robotic Systems (S0921-0296), 2021, 103(3): 1-13.
[13] Xu Z, Zhang Z J, Jiang W, et al.Fault Recovery of Distribution Network Containing Distributed Generation Based on Heuristic Search Algorithm and Multi- Population Genetic Algorithm[J]. Systems Science & Control Engineering (S2164-2583), 2018, 6(3): 100-108.

[1]	周维, 刘宇翔, 廖广平, 马鑫. 结合交并比损失的孪生网络目标跟踪算法研究[J]. 系统仿真学报, 2022, 34(9): 1956-1967.
[2]	张思贤, 杨艺, 张猛, 米鹏博. 高效的多特征自适应相关滤波跟踪器[J]. 系统仿真学报, 2022, 34(8): 1864-1873.
[3]	张子迎, 周华. 强化结构的数字壁画病害修复算法研究[J]. 系统仿真学报, 2022, 34(7): 1524-1531.
[4]	上官晋太, 党雅文, 连玮. 非刚体图像配准中一种改进的点匹配方法[J]. 系统仿真学报, 2022, 34(7): 1482-1489.
[5]	周培培, 侯幸林. 一种用于图像融合的无监督深度神经网络[J]. 系统仿真学报, 2022, 34(6): 1267-1274.
[6]	曹建芳, 贾一鸣, 闫敏敏, 田晓东. 稳定增强生成对抗网络在壁画的超分辨率重建[J]. 系统仿真学报, 2022, 34(5): 1076-1089.
[7]	冯开团, 袁杰. 基于改进注水算法的离散车间任务分配问题研究[J]. 系统仿真学报, 2022, 34(4): 768-776.
[8]	宁小娟, 李洁茹, 高凡, 王映辉. 基于最佳几何约束和RANSAC的特征匹配算法[J]. 系统仿真学报, 2022, 34(4): 727-734.
[9]	杨敬辉, 刘德康, 杜万和, 邢立宁. 基于图像特征的双目测距系统的研究[J]. 系统仿真学报, 2022, 34(3): 624-632.
[10]	刘茂山, 纪志成, 王艳, 王建锋. 核块对角表达子空间聚类及收敛性分析[J]. 系统仿真学报, 2021, 33(11): 2533-2544.
[11]	霍志浩, 金炜东, 唐鹏. 融合自监督学习的单帧图像运动视差关键点估计[J]. 系统仿真学报, 2021, 33(11): 2753-2759.
[12]	胡蕾, 王足根, 陈田, 张永梅. 一种改进的SRGAN红外图像超分辨率重建算法[J]. 系统仿真学报, 2021, 33(9): 2109-2118.
[13]	李新利, 邹昌铭, 杨国田, 刘禾. 基于生成式对抗网络的发票图像超分辨率研究[J]. 系统仿真学报, 2021, 33(6): 1307-1314.
[14]	柳有权, 裴雪, 李婉, 刘正雄. 基于邻近目标置信度评估的视觉目标跟踪与定位[J]. 系统仿真学报, 2020, 32(7): 1294-1300.
[15]	汤雯洁, 李若桐, 邓雪松, 司伟鑫, 王琼. ICP优化下的B样条四维CT图像弹性配准方法[J]. 系统仿真学报, 2020, 32(7): 1301-1311.

多机器人组合最大覆盖面积寻优及预测方法

Optimization and Prediction for Multi-robot Combination Maximum Coverage Area

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价