Adaptive strategy of collaborative robot based on double Gaussian process

To adapt to different poses of target object as well as move smoothly, an adaptive strategy that built two Gaussian processes for pose adaption and trajectory adjustment respectively was proposed. The Gaussian process for pose adaption correlated the joint angles of robot with observation variables with linear covariance function, which eliminated the vision calibration and inverse kinematic solutions; the Gaussian process for trajectory adjustment correlated different trajectory points with Gaussian kernel function, which made the adjusted trajectory move smoothly. Grasping experiment of UR3 robot with obstacles proved that the proposed method could make robot adapt to the different poses of target object and obtain smooth joint trajectories.