Robotic manipulators are critical in many applications but are known to degrade over time. This degradation is influenced by the nature of the tasks performed by the robot. Tasks with higher severity, such as handling heavy payloads, can accelerate the degradation process. One way this degradation is reflected is in the position accuracy of the robot's end-effector. In this paper, we present a prognostic modeling framework that predicts a robotic manipulator's Remaining Useful Life (RUL) while accounting for the effects of task severity. Our framework represents the robot's position accuracy as a Brownian motion process with a random drift parameter that is influenced by task severity. The dynamic nature of task severity is modeled using a continuous-time Markov chain (CTMC). To evaluate RUL, we discuss two approaches -- (1) a novel closed-form expression for Remaining Lifetime Distribution (RLD), and (2) Monte Carlo simulations, commonly used in prognostics literature. Theoretical results establish the equivalence between these RUL computation approaches. We validate our framework through experiments using two distinct physics-based simulators for planar and spatial robot fleets. Our findings show that robots in both fleets experience shorter RUL when handling a higher proportion of high-severity tasks.

翻译：机器人操作臂在许多应用中至关重要，但已知其性能会随时间退化。这种退化受机器人执行任务性质的影响。严苛度较高的任务（例如搬运重载荷）会加速退化过程。这种退化的一个体现方式是机器人末端执行器的定位精度。本文提出了一种寿命预测建模框架，该框架在预测机器人操作臂剩余使用寿命时考虑了任务严苛度的影响。我们的框架将机器人的定位精度建模为一个布朗运动过程，其随机漂移参数受任务严苛度影响。任务严苛度的动态特性使用连续时间马尔可夫链建模。为评估剩余使用寿命，我们讨论了两种方法——(1) 一种新颖的剩余寿命分布闭式表达式，以及(2) 预测文献中常用的蒙特卡洛模拟。理论结果证明了这两种剩余使用寿命计算方法的等价性。我们通过使用两种不同的基于物理的仿真器（分别针对平面和空间机器人编队）进行实验，验证了我们的框架。我们的研究结果表明，当处理高严苛度任务比例较高时，两个编队中的机器人都会经历更短的剩余使用寿命。