Abstract: Under-actuated robotic grippers, regarded as critical components of robotic grasping, have attracted considerable attention. However, existing under-actuated grippers emerge with several primary issues, including low payload, insufficient force sensing, small grasping force, weak grasping stability as well as high cost, hindering widespread applications. Some of these grippers can only implement a single grasping mode, thereby imposing restrictions on dimensional ranges of objects. To well relieve all relevant research gaps, we present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers. Second, we devise theoretical models of kinematics and power transmission based on the proposed gripper, accurately obtaining fingertip positions and contact forces. Through coupling and decoupling of five-linkage mechanisms, the proposed gripper offers the expected capabilities of grasping payload/force/stability and objects with large dimension ranges. Third, to realize the force control, an LSTM model is proposed to determine the grasping mode for synthesizing force-feedback control policies that exploit contact sensing after outlining the uncertainty of currents using a statistical method. Finally, a series of experiments are implemented to measure quantitative indicators, such as the payload, grasping force, force sensing, grasping stability and the dimension ranges of objects to be grasped. Additionally, the grasping performance of the proposed gripper is verified experimentally to guarantee the high versatility and robustness of the proposed gripper. A very promising strategy combining mechanism design and artificial intelligence (AI) technology will be highly impactful on the construction of robotic grippers. A uploaded video in YouTube: https://youtu.be/TDyCUtxnePQ.
