Brambilla Cristina, Malosio Matteo, Reni Gianluigi, Scano Alessandro
Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via Previati 1/E, 23900 Lecco, Italy.
Bioengineering Laboratory, Scientific Institute, IRCCS "Eugenio Medea", 23842 Bosisio Parini (Lecco), Italy.
Biology (Basel). 2022 Mar 2;11(3):391. doi: 10.3390/biology11030391.
In the last few years, there has been increased interest in the preservation of physical and mental health of workers that cooperate with robots in industrial contexts, such as in the framework of the European H2020 Mindbot Project. Since biomechanical analysis contributes to the characterization of the subject interacting with a robotic setup and platform, we tested different speed and loading conditions in a simulated environment to determine upper-limb optimal performance. The simulations were performed starting from laboratory data of people executing upper-limb frontal reaching movements, by scaling the motion law and imposing various carried loads at the hand. The simulated velocity ranged from 20% to 200% of the original natural speed, with step increments of 10%, while the hand loads were 0, 0.5, 1, and 2 kg, simulating carried objects. A 3D inverse kinematic and dynamic model was used to compute upper-limb kinematics and dynamics, including shoulder flexion, shoulder abduction, and elbow flexion. An optimal range of velocities was found in which the expended energy was lower. Interestingly, the optimal speed corresponding to lower exerted torque and energy decreased when the load applied increased. Lastly, we introduced a preliminary movement inefficiency index to evaluate the deviation of the power and expended energy for the shoulder flexion degree of freedom when not coinciding with the minimum energy condition. These results can be useful in human-robot collaboration to design minimum-fatigue collaborative tasks, tune setup parameters and robot behavior, and support physical and mental health for workers.
在过去几年中，人们越来越关注在工业环境中与机器人协作的工人的身心健康，例如在欧洲“人脑机器人”（H2020 Mindbot）项目的框架内。由于生物力学分析有助于表征与机器人装置和平台交互的主体，我们在模拟环境中测试了不同的速度和负载条件，以确定上肢的最佳性能。这些模拟是从人们执行上肢前伸运动的实验室数据开始进行的，通过缩放运动规律并在手上施加各种负载。模拟速度范围为原始自然速度的20%至200%，步长增量为10%，而手部负载为0、0.5、1和2千克，模拟携带的物体。使用三维逆运动学和动力学模型来计算上肢的运动学和动力学，包括肩关节前屈、肩关节外展和肘关节屈曲。我们发现了一个最佳速度范围，在该范围内消耗的能量较低。有趣的是，当施加的负载增加时，对应较低施加扭矩和能量的最佳速度会降低。最后，我们引入了一个初步的运动效率指数，以评估当不与最小能量条件一致时，肩关节前屈自由度的功率和消耗能量的偏差。这些结果对于人机协作设计最小疲劳协作任务、调整设置参数和机器人行为以及支持工人的身心健康可能是有用的。
