Ensuring the safe and reliable operation of collaborative robots demands robust sensor diagnostics. This paper introduces a methodology for formulating model-based constraints tailored for sensor diagnostics, featuring analytical relationships extending across mechanical and electrical domains. While applicable to various robotic systems, the study specifically centers on a robotic joint employing a series elastic actuator. Three distinct constraints are imposed on the series elastic actuator: the Torsional Spring Constraint, Joint Dynamics Constraint, and Electrical Motor Constraint. Through a simulation example, we demonstrate the efficacy of the proposed model-based sensor diagnostics methodology. The study addresses two distinct types of sensor faults that may arise in the torque sensor of a robot joint, and delves into their respective detection methods. This insightful sensor diagnostic methodology is customizable and applicable across various components of robots, offering fault diagnostic and isolation capabilities. This research contributes valuable insights aimed at enhancing the diagnostic capabilities essential for the optimal performance of robotic manipulators in collaborative environments.

翻译：确保协作机器人安全可靠运行需要鲁棒的传感器诊断技术。本文提出了一种专门用于传感器诊断的基于模型约束的构建方法，该方法包含跨越机械域和电气域的分析关系。虽可适用于各类机器人系统，但本研究具体聚焦于采用串联弹性致动器的机器人关节。针对串联弹性致动器施加了三种不同的约束：扭簧约束、关节动力学约束和电机约束。通过仿真实例，我们验证了所提出的基于模型传感器诊断方法的有效性。本研究探讨了机器人关节扭矩传感器可能出现的两类不同传感器故障，并深入分析了各自的检测方法。这种具有洞察力的传感器诊断方法具有可定制性，可应用于机器人各部件，具备故障诊断与隔离能力。本研究为提升协作环境中机器人操作臂最优性能所必需的诊断能力提供了重要见解。