基于集成概率模型的变阻抗机器人打磨力控制

Ensemble probabilistic model based variable impedance for robotic grinding force control

工业机器人对工件柔顺打磨作业的适应性差,为此设计机器人柔顺浮动力控末端执行器,基于集成贝叶斯神经网络模型的强化学习,提出主动自适应变阻抗的机器人打磨力控制方法.所提方法根据打磨作业的接触环境信息,利用自助法获取小量数据的多次采样样本,训练集成贝叶斯神经网络模型以描述机器人打磨系统与工况环境交互作用,采用协方差矩阵自适应进化策略(CMA-ES)求解最优阻抗参数.构建机器人打磨系统虚拟样机平台,开展叶片工件的打磨仿真实验,验证所提方法的有效性.实验结果表明,所提方法在十几次训练后,能够将打磨力的绝对跟踪误差减小至较小值,较好地实现了机器人打磨系统的主动自适应变阻抗打磨力控制,提高了机器人打磨力控制的柔顺性和鲁棒性.

A compliant floating force-controlled end-effector was designed,in order to resolve the problem of poor adaptability of industrial robots for the compliant grinding of workpieces.A robotic grinding force control method with the active adaptive variable impedance was proposed,using the reinforcement-learning based on the ensemble Bayesian neural networks model.According to the contact environment information of the robotic grinding,the multiple sampling samples from the small amount of data were obtained by the Bootstrapping method,and the ensemble Bayesian neural network model was trained to characterize the interactions between the robotic grinding system and the grinding condition environment.The optimal impedance parameters were solved by the covariance matrix adaptation evolution strategy(CMA-ES).A virtual prototype platform of the robotic grinding system was constructed.A robotic grinding simulation experiment of a blade workpiece was conducted,and the effectiveness of the proposed method was verified.Experimental results show that the proposed method reduces the absolute tracking error of the grinding force to a small value after a dozen training,realizes the active adaptive variable impedance for the grinding force control of the robotic grinding system,and improves the flexibility and the robustness of the robotic grinding force control.