社会机器人的生物灵感运动仿真:实时轨迹生成和控制方法
Marvin H Cheng1, Po-Lin Huang2, Hao-Chuan Chu2
1Department of Mechanical, Material & Aerospace Engineering, West Virginia University, Morgantown, WV 26505, USA.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
本研究介绍了一种使用卷积神经网络 (CNN) 的新型运动预测模型,用于社交机器人准确识别和模仿实时的人类运动,增强人机交互.
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
- 人与计算机的交互
背景情况:
- 社会机器人越来越多地用于医疗保健,教育和制造业,因为它们具有心理和生理上的好处.
- 有效的人机交互需要机器人准确地识别和复制实时的人类运动.
- 目前的社交机器人平台需要增强功能,以实现无和直观的互动.
研究的目的:
- 开发和验证运动预测模型,用于实时识别人类运动.
- 将该模型与多轴机器人臂集成,以实现生物灵感的运动仿真.
- 在交互式机器人系统中实施精确轨迹跟踪的控制策略.
主要方法:
- 开发一种利用卷积神经网络 (CNN) 的运动预测模型.
- 将CNN模型与多轴机器人臂集成,用于模拟人类运动.
- 应用控制策略来管理非线性并实现精确的运动跟踪.
主要成果:
- 开发的CNN模型有效地从初始状态识别出人类运动类型.
- 集成的机器人手臂通过遵循预先选择的轨迹,成功模拟识别的人类运动.
- 控制策略确保了足够的控制结果,用于精确的运动跟踪,验证系统可行性.
结论:
- 这项研究验证了一种能够有效仿真生物灵感运动的交互式机器人系统的可行性.
- 开发的系统展示了通过实时运动识别和复制增强人机交互的潜力.
- 这种方法为推进社会机器人的各种应用提供了一个有希望的方向.
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