基于改进鲸鱼算法和神经网络的食品机器人能耗轨迹规划
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黄崇富(1972—),男,重庆工程职业技术学院副教授。E-mail:huangcfho@126.com

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国家自然科学基金项目(编号:cstc2020cyj-msxmX0074)


Energy consumption path planning of food robot based on improved whale algorithm and neural network
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    摘要:

    目的:针对食品工业机器人动力学参数未知的节能轨迹规划问题,提出一种基于鲸鱼算法和神经网络的点到点机器人关节能耗轨迹规划方案。方法:构建食品机器人移动机械臂点到点关节能耗模型,设计基于神经网络的相似动力学辨识,用改进的鲸鱼算法对权重和偏置参数进行离线优化;采用四次多项式插值法规划关节移动轨迹,将轨迹参数等效为鲸鱼编码,通过求解关节能耗目标优化函数,最终得到能耗最优的关节运动规划轨迹。结果:该方案适用于机器人动力学参数未知下的轨迹规划场景,得到的轨迹能耗比同类降低约9.01%。结论:基于鲸鱼算法和神经网络的轨迹规划能实现食品机器人能耗优化目标。

    Abstract:

    Objective: In order to solve the energy-saving trajectory planning problem of robot in food industry with unknown dynamic parameters, a point-to-point robot joint energy consumption path planning scheme based on whale algorithm and neural network was proposed. Methods: The energy consumption model of point-to-point joint of mobile manipulator of food robot was constructed, the similarity dynamics identification based on neural network was designed, and the improved whale algorithm was used to optimize the weight and bias parameters off-line. The quadric polynomial interpolation method was used to plan the joint motion trajectory, and the trajectory parameters were equivalent to whale coding. By solving the joint energy consumption objective optimization function, the joint motion planning trajectory with optimal energy consumption was finally obtained. Results: The scheme was suitable for the trajectory planning scene with unknown robot dynamic parameters, and the trajectory energy consumption was reduced by about 9.01%. Conclusion: The trajectory planning based on whale algorithm and neural network can realize the energy consumption optimization goal of food robot.

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黄崇富,常宇,刘力超.基于改进鲸鱼算法和神经网络的食品机器人能耗轨迹规划[J].食品与机械,2022,(9):108-113,170.
HUANG Chong-fu, CHANG Yu, LIU Li-chao. Energy consumption path planning of food robot based on improved whale algorithm and neural network[J]. Food & Machinery,2022,(9):108-113,170.

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  • 在线发布日期: 2022-10-16
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