Pre-Touch Deformation Estimation of Soft Robotic Gripper　based on Camera Image

Soft robotic grippers are highly adaptable to various objects because they can deform and fit object shapes. However, grasping stability may change owing to the posture of the gripper while grasping an object. For a stable grasp, it is necessary to estimate the grasping posture before the grasp, namely pre-touch estimation. In particular, for soft robotic grippers, an important factor in the grasping posture is gripper deformation. In previous studies, pre-touch estimation was researched only for rigid grippers without considering deformation, and the stability of grasping an object using soft grippers was evaluated after grasping. The deformation of the gripper depends on the intrinsic characteristics of the gripper deformation (e.g., stiffness) and the contact positions between the gripper and object, that is, how the gripper can deform and where on the gripper is in contact with the object. Deformation characteristics vary from one gripper to another, and the contact positions change according to the characteristics, gripper location, and object shape. Thus, an estimation method that considers these conditions is required to achieve a pre-touch estimation of the deformation of soft robotic grippers. This study presents a vision-based method for estimating the deformation of a soft robotic gripper prior to grasping an object. The entire method is performed before the gripper grasps an object. In the first process, the deformation model that shows the manner in which the gripper can deform is defined using three approaches: discretization of the gripper based on a model of a serial chain of rigid bodies connected with a spring joint, the bending angle of the entire gripper, and piecewise constant curvature. Next, using an image, the bending angle of the entire gripper is acquired to calibrate the deformation model. Subsequently, the contact points between the gripper and object are predicted by obtaining their contours from an image. Finally, the deformation of the entire gripper is estimated based on the deformation model and predicted contact points. Three experiments were conducted to evaluate the accuracy and versatility of the proposed method with respect to gripper location and object shape.