arXiv 2412.005382024-12cs.ROcs.LGcs.SYeess.SYstat.AP

动态任务严重度下机器人操作手的预后框架

Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities

School of Industrial and Systems Engineering, Georgia Institute of Technology（工业与系统工程系，佐治亚理工学院）；Department of Mechanical and Aerospace Engineering, University of California Davis（机械与航空航天工程系，加州大学戴维斯分校）

Ayush Mohanty, Jason Dekarske, Stephen K. Robinson, Sanjay Joshi, Nagi Gebraeel

备注：: Accepted for Publication in IEEE Transactions on Systems, Man, and Cybernetics: Systems

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AI总结：

本文提出一种预后框架，通过布朗运动与连续时间马尔可夫链建模任务严重度对机器人操作手位置精度的影响，预测剩余使用寿命，并验证高严重度任务会缩短RUL。

摘要：

机器人操作手在许多应用中至关重要，但已知会随时间推移而退化。这种退化受到机器人执行任务性质的影响。严重度较高的任务，例如处理重载荷，会加速退化过程。这种退化的一种体现方式是机器人末端执行器的位置精度。在本文中，我们提出了一个预后建模框架，用于预测机器人操作手的剩余使用寿命（RUL），同时考虑任务严重度的影响。我们的框架将机器人的位置精度表示为具有随机漂移参数的布朗运动过程，该参数受任务严重度影响。任务严重度的动态特性使用连续时间马尔可夫链（CTMC）进行建模。为了评估RUL，我们讨论了两种方法——（1）一种新颖的剩余寿命分布（RLD）闭式表达式，以及（2）预后文献中常用的蒙特卡洛模拟。理论结果确立了这两种RUL计算方法之间的等价性。我们通过使用两个不同的基于物理的模拟器对平面和空间机器人车队进行实验来验证我们的框架。我们的研究结果表明，当处理高严重度任务的比例较高时，两个车队中的机器人的RUL都会缩短。

英文摘要：

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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