Finger design automation for industrial robot grippers: A review <s> Introduction <s> In support of rapid-response manufacturing, we propose a new technique for automated design and fabrication of fixture and gripper tooling for mechanical assembly. Our technique uses geometric computations on CAD part descriptions to derive form and frictional form closure tooling. In addition, we invoke rapid prototyping technology to fabricate the computed tooling directly from the derived CAD descriptions. This paper introduces the concepts of our approach to automatic tooling generation, illustrates initial results, and surveys the additional work to be done to further exploit the technique. <s> BIB001 </s> Finger design automation for industrial robot grippers: A review <s> Introduction <s> The History of Robotics, Wesley L. Stone Rigid-Body Kinematics, Gregorg S. Chirikjian Inverse Kinematics, Bill Goodwine Newton-Euler Dynamics of Robots, Mark L. Nagurka Lagrangian Dynamics, Milo?s ? Zefran and Francesco Bullo Kane's Method in Robotics, Keith W. Buffinton The Dynamics of Systems of Interacting Rigid Bodies, Kenneth A. Loparo and Ioannis S. Vakalis D-H Convention, Jaydev P. Desai Trajectory Planning for Flexible Robots, William E. Singhose Error Budgeting, Daniel D. Frey Design of Robotic End Effectors, Hodge Jenkins Sensors, Jeanne Sullivan Falcon Precision Positioning of Rotary and Linear Systems, Stephen Ludwick Modeling and Identification for Robot Motion Control, Dragan Kosti'c, Bram de Jager, and Maarten Steinbuch Motion Control by Linear Feedback Methods, Dragan Kosti'c, Bram de Jager, and Maarten Steinbuch Force/Impedance Control for Robotic Manipulators, Siddharth P. Nagarkatti and Darren M. Dawson Robust and Adaptive Motion Control of Manipulators, Mark W. Spong Sliding Mode Control of Robotic Manipulators, Hector M. Gutierrez Impedance and Interaction Control, Neville Hogan and Stephen P. Buerger Coordinated Motion Control of Multiple Manipulators, Kazuhiro Kosuge and Yasuhisa Hirata Robot Simulation, Lonnie J. Love Survey of Geometric Vision, Kun Huang and Yi Ma Haptic Interface to Virtual Environments, R. Brent Gillespie Flexible Robot Arms, Wayne J. Book Robotics in Medical Applications, Chris A. Raanes and Mohan Bodduluri Manufacturing Automation, Hodge Jenkins <s> BIB002 </s> Finger design automation for industrial robot grippers: A review <s> Introduction <s> Although parallel-jaw grippers play a vital role in automated manufacturing, gripper surfaces are still designed by trial-and-error. This paper presents an algorithmic approach to designing gripper jaws that mechanically align parts in the vertical (gravitational) plane. We consider optimal edge contacts, based on modular trapezoidal segments that maximize contact between the gripper and the part at its desired final orientation. Given the n-sided 2D projection of an extruded convex polygonal part, mechanical properties such as friction and center of mass, and initial and desired final orientations, we present an O(n3 log n) numerical algorithm to design optimal gripper jaws. We also present an O(n log n) algorithm to compute tolerance classes for these jaws, and report on an online implemented version of the algorithm and physical experiments with the jaws it designed. This paper extends earlier results that generated optimal point contacts [M. T. Zhang and K. Goldberg, “Gripper point contacts for part alignment,” IEEE Trans. Robot. Autom.18(6), 902–910 (2002)]. <s> BIB003
This paper aims to review research contributions relevant for finger design automation. Designing functional fingers of robots is one of the most complex and sensitive criteria in robotics. The current iterative and time consuming procedure of designing fingers cannot fulfill the demands of agile manufacturing. Therefore, finger design automation plays a significant role in the competitiveness of the robotic automation in the agile market. Designing gripper fingers properly can increase the workcell throughput, overcome robot inaccuracy and enhance overall system performance. Moreover, a poor finger design can damage the expensive robot hardware or the workpiece and consequently reduce the workcell throughput and reliability  . Industrial robots are designed in various sizes and payloads to fulfill a wide range of tasks. Some robots are dedicated to specific tasks, for instance welding, painting and cutting. These robots require special end-effectors called tools. Others are designed based on the task and operation environment to execute general operations, for instance assembly and pick and place. These end-effectors are the fundament of the review presented in this paper. An end-effector is the only interface between the robot and the working environment. Therefore, the overall performance of a robot highly depends on its end-effector and research in this area is of high importance for the industry. End-effectors can be divided into two major categories, hands and grippers. Hands are multi-finger end-effectors with more than one degree of freedom (DOF) per finger (see Fig. 1 (a) and (b)) and grippers usually have 2 or 3 fingers with one DOF, as shown in Fig. 1 (c) and (d) . Hands are designed for general purpose grasps while grippers are designed for more specific tasks. The most common grippers in industry are parallel-jaw which are typically actuated by pneumatic systems. In these grippers, two opposing parallel axis fingers (or jaws) move toward and away from each other by using a linear motion system that keeps both gripper attachments parallel and collinear BIB002 . Today, a sophisticated process is required to design fingers that accomplish assembly tasks. An example of essential finger design processes for a given gripper is presented in Fig. 2 . This figure is a result of the paper review, however it is presented early in order to easier explain the finger design automation process. The process in Fig.2 starts with defining the robot task which can be pick and place or assembly. In the case of an assembly task, external data regarding the assembly process are required to be imported to the system. In the next step, the 3D model or 3D vision of the workpiece are imported based on the availability of knowledge about the workpiece. Then, the type of grasp (i.e. form and force closure) is defined based on the geometrical and physical properties of the workpiece and the fingers. In the grasp synthesis and analysis process, the most suitable grasp contact sets for accomplishing the task are listed. Then fingers are designed based on the contact set. In the collision detection process, the feasibility of the designed fingers are verified by detecting possible unwanted collisions. In the occurrence of an unwanted collision, another contact set is selected. Otherwise, the fingers are experimentally verified and if they were promising the design process successfully terminates. The presented processes in Fig. 2 are categorized into groups of grasp, finger design and experimental verification (see Fig. 4 ). Completing the presented design process in Fig. 2 , which consists of an iterative trial and error procedure, may take several weeks for an expert, while the agile market demands a quick design process BIB003 . As a result, automated finger design is of the highest importance for the robotic industry in order to comply with agile manufacturing BIB001 . In this paper, each key process is critically reviewed and the most efficient methods with respect to lead-time, accuracy and accessibility are highlighted. Section 2 describes the employed methodology for the reviewing process. Results of the review are presented in section 3. Section 4 discusses the research gaps and future work.