# Grasping Assisting Algorithm in Tele-Operated Robotic Gripper
## 1 Introduction  
In recent years, robots, and specifically robotic grippers and arms, have been integrated into a wide variety of industries, working side by side with humans or replacing human activity entirely. Tele-operated systems are robotic systems, operated and controlled by a human operator from a long distance, designed to fulfill specific tasks, such as grasping an object. The act of grasping an object with a tele-operated gripper is a challenging task, mainly due to the lack of sensory information our body receives while touching an object. One of the ways to compensate for the absence of sensory feedback is by assisting the human operator with choosing a stable grasp that would ensure firm handling of the object. Our research goal is to assist operators using tele-operated systems in grasping different objects. The goal is achieved by suggesting a stable grasp solution, which is provided by an algorithm we created. The algorithm's purpose is to find the optimal force-closure grasp for a specific object. To prove that our proposed solution indeed assists the operator and improves his grasp, we tested the operator's grasp performance while mimicking our proposed solution against an unassisted case. The main contribution of this paper is the proposed algorithm, which can be implemented with a vast number of robotic grippers and applied to many objects. The first researches on grasping theory began by classifying human grasps and creating a human hand taxonomy. [PERSON] and [PERSON] [1] were among the first to differentiate between two types of grasps: power grasps that rely on security and stability, and precision grasps that rely on dexterity and sensitivity. [PERSON] et al. [2] extended that work and classified 33 different grasps into various subgroups. Their classification divided grasps into power, intermediate and precision grasps, the type of opposition (pad, palm or side opposition), and the index of the \"virtual finger\" used [3,4]. Due to the complex nature of the human hand, consisting of an overall 30 different degrees of freedom, mimicking the dynamics of the hand with robots is a complex task, so researchers turned to a simpler description of the complex hand movement, based on synergies, as described in [5; 6; 7; 8]. The advantage of using hand postural synergies can be seen in the work of [PERSON] et al. [9], who reconstructed several synergies based on measurements performed on humans grasping different objects, in order to use them in robotic movements. In his paper, [PERSON] [10] summarized and investigated the properties of form-closure and force-closure and showed the need for a quantitative quality measure of the force-closure property, so as to choose between different grasps. [PERSON], [PERSON] and [PERSON] [11] reviewed many quality measures, based on the geometry of the grasp or limitations on the contact forces, whereas [PERSON] et al. [12] focused on dimensionless measures. [PERSON], [PERSON] and [PERSON] [13] suggested a way to implant the task wrench space of the problem to create a more uniform quality measure, and [PERSON] and [PERSON] defined a grasp quality measure based on the stability of compliant contacts [14; 15].There have been various attempts to develop algorithms to find an optimal grasp. [PERSON] et al. [9] developed an algorithm based on neural network and reinforcement learning to find the optimal path to grasp an object with a robotic arm based on some quality criterions. [PERSON], [PERSON] and [PERSON] [16] found a solution based on deep learning and visual scanning of the objects to be grasped. [PERSON] and [PERSON] [17; 18] tried to deduce the optimal common grasp that would fit a number of different objects. Probably the most common of the different quality measures is the \"minimal inscribed ball in the grasp wrench space\" criterion as proposed by [PERSON] et al. [19] and [PERSON] and [PERSON] [20].