Hao-En Chang Rongshun Chen


It is crucial for the research of object localization, grasp pose estimation and the secure handling of objects to prevent damage or dropping during the pick-and-place process. This study employs the Mask R-CNN algorithm to locate the object, and to obtain the mask. Then, the mask is combined with the depth image to generate a point cloud. Subsequently, an algorithm is proposed to determine whether the object is suitable for vacuum gripper grasping. For an unsuitable case, the point cloud is fed into a PointNet++ model, which utilizes geodesic distance as the loss function to predict a grasp pose for the parallel gripper in an end-to-end manner. Additionally, to achieve stable grasping, this study arranges eight FSRs in an array configuration. By analyzing contact force information, it can detect the slippage, caused by insufficient applied force, and further compensates the gripping force. To enhance the performance of the proposed object pick-and-place system, a hand-eye calibration and a motion trajectory planning are performed. Finally, the system is tested on a six-DOF robotic arm, involving objects such as ball, bottle, paper cup, box and wooden block. The proposed system achieves a success rate of 92% in object pick-and-place over 100 experiments.

Download Statistics



Force Sensing Resistor, Grasp Pose, Object detection, PointNet++, Robotic Arm

Citation Format