基于自组织算法的情感机器人追捕任务分配

doi:10.13973/j.cnki.robot.2017.0680

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孙博寒, 王浩, 方宝富, 凌兆龙, 林杰华. 基于自组织算法的情感机器人追捕任务分配[J]. 机器人, 2017, 39(5): 680-687.DOI: 10.13973/j.cnki.robot.2017.0680. SUN Bohan, WANG Hao, FANG Baofu, LING Zhaolong, LIN Jiehua. Task Allocation in Emotional Robot Pursuit Based on Self-organizing Algorithm. ROBOT, 2017, 39(5): 680-687. DOI: 10.13973/j.cnki.robot.2017.0680.

摘要提出了基于自组织映射（self organizing map，SOM）神经网络的自组织算法，把任务分配融入网络训练过程中．通过竞争获胜函数值在网络训练（任务分配）中的决定作用，并结合算法自身任务分配的实时性，由各情感机器人根据情感等因素提供竞争获胜函数值，并对值进行强化学习调整．这样使情感直接有效地参与任务分配决策，优化了算法性能．最后，通过仿真实验验证了本文所提出算法的有效性，特别是随着情感机器人团队规模扩大，追捕时间会比现有算法缩短一半以上．

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	孙博寒
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	林杰华

关键词 ：自组织映射神经网络, 情感机器人, 任务分配, 强化学习, 竞争获胜函数

Abstract：A self-organizing algorithm based on self-organizing map (SOM) neural network is proposed and task allocation is integrated into the training process of the network. Taking advantages of the decisive effect of the competitive winning function value in network training (task allocation) and combining with the real-time performance of task allocation in the proposed algorithm, the emotional robot provides the competitive victory function value, which is adjusted by reinforcement-learning, according to the emotional factors. In this way, the emotion directly and effectively participates in task allocation decisions, and the algorithm performance is optimized. Finally, simulation experiments are carried out to verify the effectiveness of the proposed algorithm. Especially, with the scale of emotional robot team growing, the pursuit time can be shortened by more than 50% compared with the existing algorithm.

Key words： SOM (self-organizing map) neural network emotional robot task allocation reinforcement learning competitive winning function

收稿日期: 2017-03-27 录用日期: 2017-07-10

TP242

基金资助:安徽省自然科学基金（1708085MF146）；四川省科技攻关项目（2016GZ0389）；教育部创新团队项目（IRT17R32）

通讯作者: 方宝富,fangbf@hfut.edu.cn E-mail: fangbf@hfut.edu.cn

作者简介: 孙博寒(1992-),男,硕士生.研究领域:多机器人协作,情感机器人,机器学习,数据挖掘.
王浩(1962-),男,博士,教授,博士生导师.研究领域:人工智能与机器人,数据挖掘.
方宝富(1978-),男,博士,副教授,硕士生导师.研究领域:多智能体系统,情感机器人,机器学习.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0680 或 https://robot.sia.cn/CN/Y2017/V39/I5/680

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