相关实验视频
Updated: Jun 25, 2025

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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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模型对符合对象的预测操纵具有多目标优化器和对抗网络,用于遮蔽补偿
Jiaming Qi1, Peng Zhou1, Guangtao Ran2
1Centre for Transformative Garment Production, The Hong Kong University, NT, Hong Kong.
ISA transactions
|May 26, 2024
概括
本研究介绍了一种用于符合对象的新型机器人操纵框架,使用压缩表示和对抗网络来克服视觉屏蔽并改善非结构化环境中的自主交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 机器人对符合规范的物体进行操纵是具有挑战性的,因为它们的形状可变,动态复杂.
- 动态环境中的视觉阻塞阻碍了传统的操纵策略.
- 在非结构化的环境中,自主互动对机器人至关重要.
研究的目的:
- 开发一个有效的机器人操纵框架,用于在非结构化环境中的符合规范的对象.
- 解决视觉遮蔽和高维物体配置所带来的挑战.
- 为了增强机器人系统的自主交互能力.
主要方法:
- 一个基于回归的算法压缩可变形对象的配置空间.
- 一个对抗性网络整合了隐蔽补偿信息,用于指导操纵.
- 一个回落地平线估计器和模型预测控制器在安全约束下协调机器人的行动.
主要成果:
- 拟议的框架在有视觉障碍的场景中显示出卓越的操纵精度和可靠性.
- 实验验证证证实了压缩表示和阻塞补偿的有效性.
- 该方法的性能优于现有的方法在处理符合规范的对象与部分观测.
结论:
- 集成框架显著增强了在现实世界机器人应用程序中对合规对象的操作.
- 该研究强调了结合压缩表示,对抗网络和模型预测控制的潜力.
- 这项研究在复杂的,视觉上具有挑战性的环境中推进了自主机器人交互.
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