多约束条件下机器人柔性装配技能自学习

Flexible assembly skill self-learning of robot under multiple constraints

机器人的装配策略受装配对象特性、装配工艺和装配控制方法的约束,针对装配过程接触阶段的位姿不确定性问题,提出一种装配姿态调整技能自学习的方法.首先描述多约束条件下的机器人装配技能问题,建立基于力/力矩、位姿、关节角度等多模信息描述的装配系统模型;然后构建融合竞争架构的机器人决策网络和策略优化网络,通过与环境的不断交互,进行装配姿态调整技能的学习;最后,在低压电器塑料外壳卡合装配实验平台上进行测试验证,结果表明,在工件特性、装配工艺、控制规律约束下,机器人采用技能学习的方法可以获得末端姿态调整的策略,完成卡合装配,比基于深度Q学习网络(DQN)的算法成功率提高7.4%.

The assembly process of robot is constrained by the characteristics of assembly object,assembly process and assembly control law.In order to solve the problem of pose uncertainty in the contact phase of assembly process,a self-learning method of assembly post adjustment skills is proposed in this paper.Firstly,the problem of robot assembly skill under multiple constraints is described,and the assembly system model based on force/torque,posture,joint angle and other multi-mode information is built.Then the robot decision network and strategy optimization network are constructed to learn assembly pose adjustment skills through continuous interaction with the environment.Finally,the test is carried out on the low-voltage electrical appliance plastic shell fasten assembly experimental platform.The results show that under the constraints of workpiece characteristics,assembly process and control law,the skill learning method that robots adopted has obtained the end pose adjustment strategy,and completed the fasten assembly.The success rate increases by7.4 percent than the deep Q-learning network(DQN)based algorithm.