Conventionally, the arm and the hand are considered separately for robotic manipulation and grasp. The hand configuration and the wrist pose are obtained during grasp planning and afterwards, the arm configuration is found to accommodate the wrist pose. However, without considering the arm during grasp planning, this approach can become very inefficient as significant time and computation power are spent on evaluating and planning grasps that are simply unreachable. This paper presents an efficient method for obtaining a desired configuration of the arm and the hand simultaneously for precision grasping. Our method assumes that contact points and contact normals are given for the desired grasp. Inspired by the human grasp strategy, our approach implements a thumb-first strategy to narrow down the search space and increase the success rate. To precisely fulfill all fingers’ requirements, the inverse kinematics (IK) solution for the position and orientation of each finger are regarded as independent tasks. These tasks are organized in a well-designed hierarchy and the resulting joint movements from each level are combined through nullspace projection with a nullspace enlargement method to ensure the correctness of the results. Comprehensive simulation results will demonstrate that the proposed method can significantly improve the performance of classic IK algorithms.

传统上，手臂和手被分开考虑以进行机器人操纵和抓握。在抓握计划期间获得手的构造和手腕的姿势，然后，发现手臂的构造可以适应手腕的姿势。但是，在进行抓地计划时如果不考虑机械臂，这种方法可能会变得非常无效率，因为要花费大量的时间和计算能力来评估和计划根本无法达到的抓地力。本文提出了一种有效的方法，可以同时获得手臂和手的所需配置以进行精确抓握。我们的方法假设为获得所需的抓握力而指定了接触点和接触法线。受人类抓握策略的启发，我们的方法实施了“拇指先行”策略，以缩小搜索空间并提高成功率。为了精确地满足所有手指的要求，将每个手指的位置和方向的反向运动学（IK）解决方案视为独立的任务。这些任务按照精心设计的层次结构进行组织，每个级别产生的关节运动通过零空间投影与零空间扩大方法进行组合，以确保结果的正确性。全面的仿真结果表明，该方法可以显着提高经典IK算法的性能。这些任务按照精心设计的层次结构进行组织，每个级别产生的关节运动通过零空间投影与零空间扩大方法进行组合，以确保结果的正确性。全面的仿真结果表明，该方法可以显着提高经典IK算法的性能。这些任务按照精心设计的层次结构进行组织，每个级别产生的关节运动通过零空间投影与零空间扩大方法进行组合，以确保结果的正确性。全面的仿真结果表明，该方法可以显着提高经典IK算法的性能。