Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities
提出一个预测机器人操作臂剩余使用寿命的框架，考虑任务严重度对位置精度退化的影响，并用两种方法计算剩余寿命，经仿真验证高严重度任务会缩短寿命。
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 article, 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 the residual life distribution (RLD) and 2) Monte Carlo (MC) 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.