基于高斯过程的机器人自适应抓取策略

在机器人抓取作业时,目标物体的位姿经常发生变化。为了使机器人在运动过程中能够适应物体的位姿变化,提出了一种基于高斯过程的机器人自适应抓取策略。该方法建立了从观测空间到关节空间的映射,使机器人从样本中学习,省去了机器人视觉系统的标定和逆运动学求解。首先,拖动机器人抓取物体,记录物体的观测变量和机器人的关节角度;然后,利用记录的样本训练高斯过程模型,实现观测变量和关节角度的关联;最后,当得到新的观测变量时,通过训练的高斯过程模型得到机器人的关节角度。经过训练后,UR3机器人成功抓取了物体。

Adaptive grasping strategy of robot based on Gaussian process

When robot grasps an object,the pose of the object maybe change frequently.In order to make the robot adapt to the change of the pose of the object in the process of motion,an adaptive grasping strategy of robot based on Gaussian process was proposed.The proposed method maps the observation variables to the joint angles,which makes robot learn from samples and eliminates the calibration process of robot vision system and the robot inverse kinematics computation.First,the robot was dragged to grasp object.The observation variables of object and corresponding robot joint angles were recorded.Second,Gaussian process model was trained with the recorded samples,which correlates the observation variables and joint angles.Finally,after new observation variables were acquired,joint angles for grasping operation can be obtained by the trained Gaussian process model.The experiments show that UR3 robot can successfully grasp objects after training.