一个端到端计算轻量级的基于视觉的抓取系统,用于杂货商品
Thanavin Mansakul1, Gilbert Tang1, Phil Webb1
1Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了移动操纵器的高效基于视觉的抓取框架. 该系统实现了快速而准确的掌握检测,使实用机器人应用具有高成功率.
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
- 机器学习 机器学习
背景情况:
- 基于视觉的移动操纵器捕捉在感知,效率和部署方面面临挑战.
- 现有的方法往往缺乏实时应用的计算效率.
研究的目的:
- 为移动操纵器开发一个计算轻量级,端到端的掌握检测框架.
- 通过将图像坐标映射到机器人框架中来实现准确的操纵.
主要方法:
- 集成的对象检测,姿势估计和掌握点预测.
- 开发了一个从图像到机器人的坐标转换模型.
- 创建了一个基准和数据集,用于挑选和包装杂货任务.
主要成果:
- 在边缘设备上实现的平均执行时间在5秒以下.
- 在基准的1级成功率为100%,在基准的2级成功率为96%.
- 报告平均计算速度比为0.0130,表明能源效率.
结论:
- 拟议的框架是基于视觉的掌握的实用,坚固和高效的解决方案.
- 它适用于现实环境中的轻量级机器人应用.
- 该系统解决了移动操纵器感知和效率的关键挑战.
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