Optimization and Prediction for Multi-robot Combination Maximum Coverage Area

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

Abstract

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

CLC Number:

TP391.4

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.

References

[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.

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