A novel gripper design based on series elastic actuator for object recognition and manipulation

Abstract

Because of Industry 4.0 and its following releases, robotic applications are becoming more significant. The goal of using robots is to automate industrial processes and increase production yield. However, there are still study topics that need to be explored for other problems, such as safety and cooperation. Furthermore, sensor technologies are another important subject for automation. In general, sensors like encoders, cameras, lidar, and proximity are chosen for the control algorithm's feedback sensors. Many times, when only one sensor is used, sensor technologies are insufficient to identify or describe incidental obstacles. Due to this, two or more sensors may be required for continuity and safety. Alternatively, it is proposed that a gripper design with external effect sensitivity may be useful in both reducing the number of sensors and inherently sensing the external effects. For this purpose, a novel gripper mechanism design based on SEA is achieved for object recognition and manipulation. For a low-cost solution, one actuator with a ball-screw mechanism as a linear actuator is used for the fingers' positions. As it is based on SEA, the spring is placed between the linear actuator and the fingers. With this method, the finger can be actuated by one motor. However, they can be rotated independently by external effects. To estimate the external force, the length of the spring is computed by using absolute encoders. As a result of these, the proposed gripper mechanism is sensitive to external effects and can be used for estimating force without any force/torque sensor or tactile sensor. For object recognition, the proposed gripper interacts with the objects placed at the workspace. However, this is not enough to recognize an object. Hence, a DNN model is needed to interpret the interaction between the gripper and an object. Therefore, a DNN model is created in order to achieve recognition by using the points on the defined objects' surfaces. For the training part of DNN, a synthetic data set is generated via CloudCompare. As a result of different hyperparameters' effects on the DNN model, the best model is achieved for the recognition of 11 objects. The experiments are conducted in MEAM laboratory with the gripper mounted on the Staubli Rx160 robot arm. It is proposed for object manipulation that the gripper has the ability to compensate for position faults caused by controller error, an inaccurate model, and so on. The proposed gripper can successfully perform common industrial tasks such as peg-in-hole and surfaces following in collaborative applications. To prove this approach, the experiments are conducted with a haptic device and the gripper mounted on the Staubli Rx160 robot arm and used untrained operators. The results are compared according to control strategies. For this purpose, the user operates the tasks in cases of no guidance and a rigid gripper mechanism, guidance and a rigid gripper mechanism, and a series elastic gripper mechanism with guidance.