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An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

Author

Listed:
  • Hongwei Tang

    (Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang 422000, China
    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Anping Lin

    (College of Electronic Information and Electrical Engineering, Xiangnan University, Chenzhou 423000, China)

  • Wei Sun

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Shuqi Shi

    (Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang 422000, China)

Abstract

The methods of task assignment and path planning have been reported by many researchers, but they are mainly focused on environments with prior information. In unknown dynamic environments, in which the real-time acquisition of the location information of obstacles is required, an integrated multi-robot dynamic task assignment and cooperative search method is proposed by combining an improved self-organizing map (SOM) neural network and the adaptive dynamic window approach (DWA). To avoid the robot oscillation and hovering issue that occurs with the SOM-based algorithm, an SOM neural network with a locking mechanism is developed to better realize task assignment. Then, in order to solve the obstacle avoidance problem and the speed jump problem, the weights of the winner of the SOM are updated by using an adaptive DWA. In addition, the proposed method can search dynamic multi-target in unknown dynamic environment, it can reassign tasks and re-plan searching paths in real time when the location of the targets and obstacle changes. The simulation results and comparative testing demonstrate the effectiveness and efficiency of the proposed method.

Suggested Citation

  • Hongwei Tang & Anping Lin & Wei Sun & Shuqi Shi, 2020. "An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments," Energies, MDPI, vol. 13(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3296-:d:376893
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    References listed on IDEAS

    as
    1. Yajing Gao & Yanping Sun & Xiaodan Wang & Feifan Chen & Ali Ehsan & Hongmei Li & Hong Li, 2017. "Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique," Energies, MDPI, vol. 10(12), pages 1-20, December.
    2. Thiago Gomes Leal Ganhadeiro & Eliane Da Silva Christo & Lidia Angulo Meza & Kelly Alonso Costa & Danilo Pinto Moreira de Souza, 2018. "Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps," Energies, MDPI, vol. 11(10), pages 1-14, October.
    3. Bizhong Xia & Yadi Yang & Jie Zhou & Guanghao Chen & Yifan Liu & Huawen Wang & Mingwang Wang & Yongzhi Lai, 2019. "Using Self Organizing Maps to Achieve Lithium-Ion Battery Cells Multi-Parameter Sorting Based on Principle Components Analysis," Energies, MDPI, vol. 12(15), pages 1-17, August.
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