基于示教学习和自适应力控制的机器人装配研究

Robotic assembly based on learning from demonstration and adaptive force control

针对柔性自动化领域的机器人装配问题,对示教学习和自适应力控制等方面进行了研究,对初始位置变化时示教搜孔、插孔时降低接触力矩波动速度和误差的策略进行了归纳,提出了利用示教学习对搜孔轨迹泛化和模糊自适应阻抗控制插孔的方法。首先根据是否与孔产生接触力将示教任务分为两段;接着利用了任务参数化的高斯混合模型(TP-GMM)训练并泛化第一段轨迹;最终和原示教的第二段轨迹组合为新的搜孔轨迹;采用了六自由度阻抗控制使得机器人具有柔顺性,再利用了模糊自适应策略调节阻抗控制中Z轴期望接触力,利用UR机器人对方形孔进行了装配试验验证。研究结果表明:所提出的策略在新的初始位置,仍能绕过障碍物并生成新的搜孔轨迹,无需再次示教;调节期望接触力相比其不变时,绕X轴方向力矩波动速度低,且波动误差减小了30%。

Aiming at robotic assembly of flexible automation,learning from demonstration and adaptive force control were investigated. The strategies of searching the hole by the human demonstration when the initial position of hole changed and reducing the fluctuation speed and error of contact torque error when peg in hole were induced were summarized. A method of searching the hole by using learning from demonstration and peg in hole with adaptive force control was proposed. Firstly, the trajectory was divided into two segments according to whether there existing contact force between peg and hole. Task-parameterized gaussian mixture model(TP-GMM)was used to generalize the first trajectory, then combined the second trajectory to generate new trajectory. Secondy,six degrees of freedom impedance control made robot compliant,and fuzzy adaptive control was used to change Z-axis desired contact force of impedance control. Finally, assembly task was tested on UR5 robot. The results indicate that the generated trajectory can successfully search for hole and demonstration is no need again. The fluctuation speed of contact torque around X-axis reduces, and fluctuation error reduces by thirty percent comparing to the constant desired contact force.