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SafeNet: A methodology for integrating general-purpose unsafe devices in safe-robot rehabilitation systems

机译:SafeNet:一种将通用型不安全设备集成到安全机器人康复系统中的方法

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Robot-assisted neurorehabilitation often involves networked systems of sensors ("sensory rooms") and powerful devices in physical interaction with weak users. Safety is unquestionably a primary concern. Some lightweight robot platforms and devices designed on purpose include safety properties using redundant sensors or intrinsic safety design (e.g. compliance and backdrivability, limited exchange of energy). Nonetheless, the entire "sensory room" shall be required to be fail-safe and safely monitored as a system at large. Yet, sensor capabilities and control algorithms used in functional therapies require, in general, frequent updates or re-configurations, making a safety-grade release of such devices hardly sustainable in cost-effectiveness and development time. As such, promising integrated platforms for human-in-the-loop therapies could not find clinical application and manufacturing support because of lacking in the maintenance of global fail-safe properties. Under the general context of cross-machinery safety standards, the paper presents a methodology called SafeNet for helping in extending the safety rate of Human Robot Interaction (HRI) systems using unsafe components, including sensors and controllers. SafeNet considers, in fact, the robotic system as a device at large and applies the principles of functional safety (as in ISO 13489-1) through a set of architectural procedures and implementation rules. The enabled capability of monitoring a network of unsafe devices through redundant computational nodes, allows the usage of any custom sensors and algorithms, usually planned and assembled at therapy planning-time rather than at platform design time. A case study is presented with an actual implementation of the proposed methodology. A specific architectural solution is applied to an example of robot-assisted upper-limb rehabilitation with online motion tracking. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
机译:机器人辅助的神经康复通常涉及与弱用户进行物理交互的传感器(“感官室”)和功能强大的设备的联网系统。安全无疑是首要问题。特意设计的某些轻型机器人平台和设备包括使用冗余传感器的安全属性或本质安全设计(例如,合规性和反向驱动性,有限的能量交换)。但是,整个“感官室”应被要求是故障安全的,并作为一个整体系统被安全地监视。然而,在功能疗法中使用的传感器功能和控制算法通常需要频繁的更新或重新配置,从而使得此类设备的安全级别发布在成本效益和开发时间上难以持续。因此,由于缺乏对全局故障安全特性的维护,有希望的用于环环疗法的集成平台无法找到临床应用和制造支持。在跨机器安全标准的大背景下,本文提出了一种称为SafeNet的方法,该方法可帮助扩展使用不安全组件(包括传感器和控制器)的人机交互(HRI)系统的安全率。实际上,SafeNet将机器人系统视为一个整体设备,并通过一系列体系结构程序和实施规则来应用功能安全性原则(如ISO 13489-1)。通过冗余的计算节点监视不安全设备网络的功能,允许使用通常在治疗计划时而不是平台设计时计划和组装的任何自定义传感器和算法。案例研究给出了所提出方法的实际实施。特定的体系结构解决方案应用于带有在线运动跟踪的机器人辅助上肢康复的示例。 (C)2014 Elsevier Ireland Ltd.保留所有权利。

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