Abstract
As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM)-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU) sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC) and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.
Highlights
In the last few decades, robotic applications have drastically increased, and continue to increase, as we advance towards more sophisticated and fast-paced development environments
We have presented a novel approach for fault diagnosis using a Support Vector
We tested the method on a crawling–rolling reconfigurable robot named Scorpio
Summary
In the last few decades, robotic applications have drastically increased, and continue to increase, as we advance towards more sophisticated and fast-paced development environments. Modern robots are highly complex mechatronic systems with hardware and software modules that have a diverging set of features. Due to the highly complex nature of next-generation robotic systems, and the uncertain environments they occupy, modern robots are highly likely to encounter faults during runtime. Even well-designed robotic hardware will encounter a fault in its lifetime. Machine learning techniques have been used for automated fault diagnosis in many industrial applications. In [1], the authors proposed a fault diagnosis method for a spur bevel gear box. The statistical features used in this procedure are determined via wavelet coefficients of the vibration signals. This work uses Artificial Neural Networks (ANN), which provided an accuracy of 97.5%, and Proximal Support
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