考虑任务强度动态变化，预测机械臂剩余寿命
Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities
- 用带随机漂移的布朗运动建模末端位置精度退化
- 任务严重性由连续时间马尔可夫链描述，影响寿命预测
- 在两类仿真环境中验证，高负荷任务显著缩短寿命
机器人机械臂在各类应用中至关重要，但会随时间退化。任务严重性（如搬运重载）会加速退化过程，表现为末端执行器位置精度下降。本文提出一种预测框架，用于估计机械臂剩余使用寿命（RUL），并考虑任务严重性的动态影响。将机器人位置精度建模为带有随机漂移参数的布朗运动，任务严重性的动态变化通过连续时间马尔可夫链（CTMC）刻画。提出了两种计算RUL的方法：(1) 一种新颖的闭式剩余寿命分布（RLD）表达式；(2) 常用于故障预测研究的蒙特卡洛模拟。理论分析证明了两种方法等价。通过两个基于物理的仿真平台（平面与空间机械臂群）进行实验验证。结果表明，在两类机械臂中，执行高严重性任务比例越高，剩余使用寿命越短。
原文摘要 · Abstract (English)
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.
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