Precision fingertip grasp: A human-inspired grasp planning and inverse kinematics approach for integrated arm-hand systems

In this paper, we present a human-inspired approach to solve grasp planning and inverse kinematics (IK) problems, simultaneously. Our proposed solution is for integrated arm-hand systems. Conventional approaches consider the robot manipulator (arm) and the robotic hand separately and solve the problems of grasp planning and IK in sequence. Such separate considerations of the arm and hand often introduce errors in the IK solution. The sequential approaches waste significant computational power in searching for infeasible grasps. To address these issues, we propose to consider the robotic arm and the hand as a kinematically integrated system. We then introduce a coarse-to-fine strategy to solve grasp planning and IK problems simultaneously. The proposed approach achieves force-closure fingertip grasping without using reachability information a priori. Instead, through an integrated grasp planning and IK solution, the reachability information is obtained from the IK solution and is used to filter out infeasible grasps. This strategy will dramatically reduce the search space and save significant computational power. Numerical examples will be used to demonstrate the efficiency of the proposed approach, in comparison to a sequential solution of the grasp planning and IK for integrated arm-hand systems.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a human-inspired approach to simultaneously solve grasp planning and inverse kinematics problems for integrated arm-hand systems is proposed. This approach treats the arm and hand as a kinematically integrated system and utilizes a coarse-to-fine strategy. By integrating grasp planning and inverse kinematics, the proposed method achieves force-closure fingertip grasping and reduces computational power.