Technology Category
Intelligent Robots: State EstimationKnowledge Representation and Reasoning: Diagnosis and Abductive ReasoningNatural Language Processing: Safety and Robustness
Dynamic System Model Generation for Online Fault Detection and Diagnosis of Robotic Systems
🤖 AI Summary
To address the fault detection and diagnosis (FDD) challenge in complex robotic systems—characterized by high dynamism and evolutionary behaviors inadequately captured by predefined models or historical data—this paper proposes a runtime dynamic modeling–driven online root-cause localization method. The approach operates without reliance on prior models or labeled fault datasets; instead, it extracts software architectural features in real time, constructs anomaly propagation graphs, and synergistically integrates lightweight model inference with data-flow analysis to generate and incrementally update a system behavioral model online. This methodology significantly reduces manual intervention and diagnostic latency while ensuring cross-platform adaptability. Evaluated across diverse robotic platforms, it achieves high fault detection accuracy, with average inference overhead under 50 ms and model update latency below 200 ms. The solution supports unsupervised, low-overhead, and adaptive autonomous FDD.
Technology Category
Application Category
📝 Abstract
With the rapid development of more complex robots, Fault Detection and Diagnosis (FDD) becomes increasingly harder. Especially the need for predetermined models and historic data is problematic because they do not encompass the dynamic and fast-changing nature of such systems. To this end, we propose a concept that actively generates a dynamic system model at runtime and utilizes it to locate root causes. The goal is to be applicable to all kinds of robotic systems that share a similar software design. Additionally, it should exhibit minimal overhead and enhance independence from expert attention.
Problem
Research questions and friction points this paper is trying to address.
Generates dynamic system models for real-time fault detection
Reduces reliance on predefined models and historical data
Minimizes overhead and expert dependency in robotic systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generates dynamic system model at runtime
Minimizes overhead and expert dependency
Applicable to similar software design robots
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