Dexclar: A gripper platform for payload-centric manipulation and dexterous applications

Developing grasping devices with the capabilities to carry out dexterous tasks similar to human hand are being studied for many decades. To this aim, mathematical analysis such as control of multi-fingered gripper, grasp synthesis algorithms, contact types and their interactions have been explicitly addressed by many researchers. Since human hands are dexterous due to the complex integration of control and numerous sensors, hence they are naturally adaptable to grasp, in-hand manipulation of plurality of object by their construction. On the other hand, artificial grippers require priori knowledge of the payload geometry and configuration to maneuver grasping and manipulation tasks at the very first place. Moreover, theoretical analysis, such as contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore, uncertainties are always present to restrict a maneuver, even though the gripper is kinematically feasible of doing the task. Hence, in general, industrial grippers do exploit simpler mechanisms with least number of fingers and tend to avoid soft materials in the construction primarily to achieve dexterity, reliability, repeatability and speed in the process. However, in-hand manipulation of objects urges certain degrees of flexibility in the gripper design; which is difficult to obtain from a rigid structure and also the use of non-rigid materials reduce speed, accuracy and performance. In this research, a gripper platform named Dexclar (DEXterous reConfigurable moduLAR) is proposed, which addresses the dilemma by combining mechanism and modularity, evaluating payload centric requirements.