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iAPF:一个改进的人工潜力场框架,用于不对称的双臂操纵,实时避免双臂碰撞
S K Surya Prakash1, Darshankumar Prajapati2, Bhuvan Narula3
1School of Mechanical and Materials Engineering, Indian Institute of Technology Mandi, Mandi, India.
Frontiers in robotics and AI
|November 13, 2025
概括
本研究介绍了一种基于视觉的双臂机器人运动规划框架,通过新的稳定和避免碰撞技术提高了工业任务的安全性和效率,例如组件分类.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 控制系统 控制系统
背景情况:
- 双臂操纵器需要复杂的运动计划来完成复杂的任务.
- 现有的方法经常在实时避免碰撞和类似人类的操纵方面扎.
研究的目的:
- 为双臂操纵器开发一个强大的基于视觉的运动规划框架.
- 为了提高操纵精度,确保避免臂间碰撞,并实现不对称的任务执行.
主要方法:
- 一种混合控制策略,它结合了改进的人工潜能场 (iAPF) 的线性速度和角速度的比例导数 (PD) 控制.
- 一个基于优先级的状态机器,用于类似人类的非对称双臂控制.
- 实时视觉系统 (YOLOv8 OBB) 和计算效率高的连续距离计算用于碰撞监控.
主要成果:
- 该框架实现了对所需配置的非对称趋同,通过利亚普诺夫稳定性分析验证.
- 实时视觉系统在20 FPS显示出高检测精度 (0.99/0.97).
- 与传统的APF相比,比较测试显示了更光滑的轨迹,优化的空间分离以及有效的碰撞预防.
结论:
- 拟议的基于视觉的框架为双臂机器人提供了稳定运动计划.
- 它显著提高了工业任务的安全性和效率,例如组件分类.
- 该方法允许精确,类似人类的操纵,同时防止碰撞.
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