在基于触觉感知的机器人抓取中检测从稳定到不稳定的过渡
Zhou Zhao1,2, Dongyuan Zheng1, Lu Chen3
1School of Computer Science, Central China Normal University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了机器人实时动态状态传感网络,使用触觉图像预测负载操作期间的稳定性变化. 该系统准确地检测从稳定状态到不稳定状态的过渡,提高机器人的安全性.
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
- 传感器技术 传感器技术
背景情况:
- 机器人执行复杂的负载操作,这可能导致从稳定状态到不稳定状态的动态转移.
- 监控这些转变对于安全高效的机器人任务执行至关重要.
研究的目的:
- 为机器人开发一个实时动态状态传感网络.
- 准确预测和分类机器人在载荷处理过程中的机器人操作稳定性的变化.
主要方法:
- 在机器人对象交互期间利用触觉传感器的触觉图像.
- 集成卷积神经网络 (CNN) 用于空间特征提取和长短期记忆 (LSTM) 网络用于时间信息.
- 收集了从稳定状态到不稳定状态的过渡数据集,并采用了移动窗口方法进行框架采样.
主要成果:
- 实现了实时时间序列预测,推断步骤为31.84 ms.
- 在预测状态变化方面达到98.90%的平均分类准确度.
- 在以前从未见过的物体上表现出高精度的强度.
结论:
- 拟议的网络有效地感知和预测负载操作期间机器人的动态状态变化.
- 该系统提供了一种可靠的解决方案,用于在现实应用中提高机器人安全性和性能.
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