五指机械手通过接触点优化实现稳定抓握，精度高且响应快。
Contact-based Grasp Control and Inverse Kinematics for a Five-fingered Robotic Hand
- 基于接触点优化与逆运动学结合控制
- 非拇指指节效率0.966-0.996，定位误差<0.0283m
- 适合需要精准抓取的机器人操作场景
本文实现并分析了一种结合接触点控制与逆运动学求解的五指机械手抓取系统。在PyBullet仿真环境中使用DexHand v2模型，通过接触点优化与力闭合验证，实现了稳定抓握。方法在非拇指指节上取得0.966至0.996的运动效率，拇指为0.879；非拇指指节定位误差在0.0267至0.0283米之间，拇指为0.0519米。系统在240Hz仿真频率下实现快速位置稳定，整个抓取过程保持接触状态稳定。实验验证了方法有效性，同时指出拇指对位和水平面控制仍有改进空间。
原文摘要 · Abstract (English)
This paper presents an implementation and analysis of a five-fingered robotic grasping system that combines contact-based control with inverse kinematics solutions. Using the PyBullet simulation environment and the DexHand v2 model, we demonstrate a comprehensive approach to achieving stable grasps through contact point optimization with force closure validation. Our method achieves movement efficiency ratings between 0.966-0.996 for non-thumb fingers and 0.879 for the thumb, while maintaining positional accuracy within 0.0267-0.0283m for non-thumb digits and 0.0519m for the thumb. The system demonstrates rapid position stabilization at 240Hz simulation frequency and maintains stable contact configurations throughout the grasp execution. Experimental results validate the effectiveness of our approach, while also identifying areas for future enhancement in thumb opposition movements and horizontal plane control.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。