<ul data-eligibleForWebStory="false"><li>Researchers propose an Adaptive Gaussian Mixture Models-based Anomaly Detection system for Cable-Driven Parallel Robots without additional sensors.</li><li>The system uses motor torque data to detect anomalies that could affect robot performance during load manipulation tasks with predefined toolpaths.</li><li>An adaptive, unsupervised outlier detection algorithm based on Gaussian Mixture Models is employed, showing high accuracy in detecting anomalies with minimal latency.</li><li>Validation tests demonstrate a 100% true positive rate, 95.4% average true negative rate, and increased robustness compared to other detection methods.</li></ul>

Adaptive Gaussian Mixture Models-based Anomaly Detection for under-constrained Cable-Driven Parallel Robots

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