一种混合弹性-超弹性方法,用于模拟软触觉传感器
Berith Atemoztli De la Cruz Sánchez1, Jean-Philippe Roberge1
1Command and Robotics Laboratory, École de Technologie Supérieure, Montreal, QC, Canada.
Frontiers in robotics and AI
|August 6, 2025
概括
为机器人掌握产生现实的触觉数据至关重要. 这项研究引入了使用有限元分析和卷积神经网络 (CNN) 的混合模拟方法,以高效地创建准确的合成触觉图,平衡速度和精度.
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
- 计算力学 计算力学 计算力学
背景情况:
- 有效的机器人抓取需要大量的触觉数据,这是很难获得的.
- 模拟触觉传感器在平衡模拟精度与处理时间方面提出了挑战.
- 人工智能 (AI) 和触觉感应是先进机器人能力的关键.
研究的目的:
- 开发一种混合模拟方法,用于生成软电容触觉传感器的合成触觉图.
- 在触觉传感器模拟中解决准确性和处理时间之间的权衡问题.
- 为了使人工智能机器人训练能够高效准确地生成触觉数据.
主要方法:
- 一种混合有限元模拟,结合了弹性和超弹性模型.
- 卷积神经网络 (CNN) 的集成用于合成触觉地图生成.
- 自动评估接触贴片的复杂性,以选择合适的模拟模型.
主要成果:
- 通过超弹性模型达到高达97%的结构相似性指数 (SSIM) 测量,通过对未见物体的弹性模型达到90%.
- 证明了模拟速度和准确性之间的自适应平衡.
- 成功训练,验证和测试了管道,使用了53,400个现实世界触觉地图的数据集.
结论:
- 拟议的混合方法有效地为软电容传感器生成合成触觉地图.
- 这种方法提供了一种灵活的解决方案,用于在不同的精度和复杂性要求下生成触摸数据.
- 这种方法提高了使用模拟触觉数据用于人工智能驱动的机器人抓取的可行性.
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