Ranaldi Simone, Bibbo Daniele, Corvini Giovanni, Schmid Maurizio, Conforto Silvia
Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy.
Front Neurorobot. 2023 Jun 22;17:1183164. doi: 10.3389/fnbot.2023.1183164. eCollection 2023.
Human robot collaboration is quickly gaining importance in the robotics and ergonomics fields due to its ability to reduce biomechanical risk on the human operator while increasing task efficiency. The performance of the collaboration is typically managed by the introduction of complex algorithms in the robot control schemes to ensure optimality of its behavior; however, a set of tools for characterizing the response of the human operator to the movement of the robot has yet to be developed.
Trunk acceleration was measured and used to define descriptive metrics during various human robot collaboration strategies. Recurrence quantification analysis was used to build a compact description of trunk oscillations.
The results show that a thorough description can be easily developed using such methods; moreover, the obtained values highlight that, when designing strategies for human robot collaboration, ensuring that the subject maintains control of the rhythm of the task allows to maximize comfort in task execution, without affecting efficiency.
人机协作在机器人技术和人机工程学领域正迅速变得重要,因为它能够降低人类操作员的生物力学风险,同时提高任务效率。协作的性能通常通过在机器人控制方案中引入复杂算法来管理,以确保其行为的最优性;然而,一套用于表征人类操作员对机器人运动响应的工具尚未开发出来。
在各种人机协作策略中测量躯干加速度,并用于定义描述性指标。递归量化分析用于构建躯干振荡的紧凑描述。
结果表明,使用这些方法可以轻松地进行全面描述;此外,获得的值突出表明,在设计人机协作策略时,确保受试者保持对任务节奏的控制能够在不影响效率的情况下最大限度地提高任务执行的舒适度。