基于隐马尔可夫的机械臂浅深度插入控制策略

Shallow-depth insertion control strategy of mechanical arm based on hidden Markov

针对浅深度插入装配机械臂难以操控的问题,提出了一种基于隐马尔可夫的机械臂浅深度插入控制策略。首先,对浅深度插入操作进行了分析,通过采集人工操作数据,获得了力控的参数;其次,基于实测的人工操作数据,识别了力控参数的时序数据,并采用阻尼控制律对人工操作过程进行了建模;然后,利用隐马尔可夫模型对力控参数的时间序列数据进行了建模,得到了适用于机械臂浅深度插入的控制策略(该策略以隐马尔可夫模型在各个状态下的符号输出概率表示);最后,在多种应用场景中,采用UR106自由度机械臂分别开展了机械臂浅深度插入实验。研究结果表明:在机械臂装配过程中,采用该策略能够精准获得人工操作过程的阻尼控制参数情况,得到隐马尔可夫相对于各控制参数的标准差均值分布,从而实现其对多种应用场景的自如操控,这将对机械臂浅深度插入装配的自主执行提供有效的理论基础。

Aiming at the difficult control problem of assembly manipulator with shallow depth insertion,a hidden Markov based shallow depth insertion control strategy for manipulator was proposed.Firstly,the shallow depth insertion operation was analyzed,and the force control parameters were obtained by collecting manual operation data.Secondly,based on the measured manual operation data,the time series data of force control parameters were identified,and the manual operation process was modeled by using damping control law.Then,the hidden Markov model was used to model the time series data of the force control parameters,so as to obtain the control strategy suitable for the shallow depth insertion of the manipulator.The strategy was expressed by the symbolic output probability of the hidden Markov model in each state.Finally,UR106-DOF robotic arm was used to conduct shallow depth insertion experiments in various application scenarios.The results show that mechanical arm can accurately obtain damping control parameters of the process of manual operation,get hidden Markov relative to the mean distribution of the standard deviation of the control parameters,thus realizing its application to various scenarios,which provides an effective theoretical basis for the autonomous execution of the shallow-depth insertion assembly of the mechanical arm.