The primary motivation of this study is to develop a cost-effective, safe, easily controlled, and passively adaptive robotic gripper. A novel linear series elastic actuated robotic gripper (LSEA-RG) is proposed to accomplish the aforementioned goals. The LSEA-RG has the ability to adaptively grasp objects with different shapes and sizes by grasping force control. First, the mechanical design, including the transmission scheme, the finger mechanism, and the actuating mechanism is presented. Subsequently, the kinematic analysis is performed to explore the transmission characteristic of the actuating displacement and force. Then a model-free control method based on the lookup functions of the kinematic transmission characteristics is presented to achieve the grasping range and force control. Finally, several grasping experiments are carried out based on the LSEA-RG prototype to evaluate the grasping performance. The results demonstrate that the presented grasping control method which based on the lookup functions is valid. The LSEA-RG could estimate the grasping force without the help of a fingertip force sensor and grasp objects with different shapes and hardness adaptively.

The primary motivation of this study is to develop a sensor-less, easily controlled, and passively adaptive robotic gripper. A back-drivable pneumatic underactuated robotic gripper (PURG), based on the pneumatic cylinder and underactuated finger mechanism, is presented to accomplish the above goals. A feedforward grasping force control method, based on the learned kinematics of the underactuated finger mechanism, is proposed to achieve sensor-less grasping force control. To enhance the grasping force control accuracy, a state-based actuating force modeling method is presented to compensate the hysteresis error which exists in the transmission mechanism. Actuating force control experiment is performed to validate the effectiveness of the state-based actuating pressure modeling method. Results reveal that compared with the non-state-based modeling method, the proposed state-based actuating force modeling method could reduce the modeling error and control error by about 37.0% and 77.2%, respectively. Results of grasping experiments further reveal that grasping force could be accurately controlled by the state-based feedforward control model in a sensor-less approach. Adaptive grasping experiments are performed to exhibit the effectiveness of the sensor-less grasping force control approach.