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相关概念视频

One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

792
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
792

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相关实验视频

Updated: Jan 11, 2026

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
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基于EPSO的刚性机器人手臂用于避开障碍物,抓住物体.

Tong Xu1, Xiaomin Han2, Honghui Liu2

  • 1School of Information Technology, Jiangsu Open University, Nanjing, 210000, China. xutong0901@126.com.

Scientific reports
|November 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的机器人抓取框架,用于采摘水果,改善对象识别和避开障碍. 该EPSO-TPPP-YOLOv8系统确保安全和高效的水果收获与最小的代.

关键词:
避开对象的方法抓住物体抓住物体路径规划 路径规划 路径规划刚性机器人手臂是一个硬的机器人手臂.安全距离分析安全距离分析

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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
  • 人工智能的人工智能

背景情况:

  • 机器人水果采摘在物体识别准确性,性能效率和避免障碍碰撞方面面临挑战.
  • 现有的系统经常与复杂的果园环境和精确的抓取机动作斗争.

研究的目的:

  • 提出和评估一个新的对象抓取框架,EPSO-TPPP-YOLOv8,用于机器人果.
  • 为了提高机器人手臂在智能果园应用中的障碍回避和路径规划.

主要方法:

  • 开发了机器人手臂和障碍物 (圆柱体和球体) 的安全距离分析模型.
  • 实施了用于机器人手臂路径规划的增强的粒子群集优化 (EPSO) 算法.
  • 使用YOLOv8.8构建了一个独特的数据集,用于基于视觉的物体识别.

主要成果:

  • 在没有人类干预的情况下,EPSO算法在50次代内成功识别了安全路径.
  • 在人类指导下,EPSO-TPPP框架在10次代内实现了安全路径规划.
  • 模拟和实地实验验证了机器人手臂的避障抓取功能.

结论:

  • EPSO-TPPP-YOLOv8框架为智能果园中的机器人果摘提供了可靠和高效的解决方案.
  • 拟议的方法显著改善了避免障碍和路径规划,这对于自主收获至关重要.