运动预测和验证,考虑感知风险的基于三维碰撞避免的三维碰撞
1Department of Mechanical Engineering, Human-Centric Design Research Lab, Texas Tech University, Lubbock, TX, USA.
概括
在避开障碍时预测人手臂运动现在包括感知风险,增强数字人体模型. 这种认知因素可以提高运动预测的准确性,从而提高人体工程学和机器人的安全性.
科学领域:
- 生物力学和人类运动分析
- 认知科学与风险感知
- 机器人和人机交互的人机交互
背景情况:
- 预测人类上肢运动对于数字人体建模至关重要,尤其是在涉及避障任务时.
- 现有的模型往往缺乏像感知风险这样的认知因素的整合,限制了它们的预测准确性.
- 了解人类如何根据感知风险调整运动是提高以人为中心的应用程序安全性和效率的关键.
研究的目的:
- 开发和验证一个基于优化的运动预测框架,用于人类上肢运动的3D碰撞避免任务.
- 用贝叶斯决策理论将生物力学约束与认知感知风险集成到运动预测模型中.
- 通过将感知到的风险纳入传统的人工接触领域之外,推进数字人体建模.
主要方法:
- 收集了实验数据,这些实验对象在执行各种特性的3D障碍物达到任务时进行了实验.
- 开发了一个优化公式,使用B-Spline曲线用于关节角度,最大限度地减少关节位移,最大限度地提高端效应器速度.
- 将感知到的与风险相关的约束纳入优化框架,修改了排除认知因素的基线模型.
主要成果:
- 包括感知风险在内显著改善了在障碍物周围的最低清除距离的预测.
- 人类对象在脆弱物体周围表现出更大的清除距离,表现出受感知风险影响的谨慎行为.
- 预测方法通过将模拟的关节角度配置文件与实验数据进行比较来验证,显示出良好的一致性.
结论:
- 将感知风险集成到运动预测框架中,可以显著提高数字人类模型在避免碰撞任务中的准确性.
- 这种基于认知的方法提供了更现实的人类运动表现,超越了纯粹的生物机械或几何约束.
- 这些发现在人体工程学,康复和人机交互方面具有广泛的应用,改善工作空间设计,安全协议和协作机器人.
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