Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities

2024-11-30

arXiv.org

AI Summary

To address the challenge of predicting the remaining useful life (RUL) of robotic manipulators under dynamically varying task severity, this paper proposes a stochastic degradation modeling framework that explicitly incorporates task-time-varying characteristics. Specifically, end-effector positioning accuracy degradation is modeled as a task-severity-modulated random-drift Brownian motion, while task severity evolution is characterized by a continuous-time Markov chain (CTMC). We derive, for the first time, a closed-form RUL distribution—termed the remaining lifetime distribution (RLD)—that rigorously accounts for dynamic task severity, and formally prove its equivalence to Monte Carlo simulation results. Evaluations on planar and spatial robotic simulation platforms demonstrate that a 10% increase in high-severity task proportion reduces mean RUL by 18.3%; moreover, our method achieves a 27% reduction in RUL prediction error compared to baseline approaches.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Life-Long and Continual Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Predicts robotic manipulator degradation under dynamic task severity conditions
• Models position accuracy as Brownian motion with task-dependent drift
• Evaluates remaining useful life using analytical and simulation methods

Innovation

• Brownian motion models robot degradation with random drift
• Continuous-time Markov chain captures dynamic task severity
• Closed-form expression and Monte Carlo estimate remaining useful life