automated manufacturing operators

Abstract: The problem of nonprehensile robotic catching is addressed using virtual nonholonomic constraints (VNHCs); the motion of the robot end-effector is designed and controlled directly in terms of the states of the object before it is caught on the surface of the end-effector. In contrast to contemporary approaches, our approach avoids the need for object trajectory prediction, and consequent time-varying robot trajectory generation. With a focus on the planar case, we demonstrate that our VNHC approach can be used to catch an object on an end-effector at a desired height, with minimal relative normal velocity at the contact instant. Moreover, the VNHC function depends only on the desired catching height, and the same VNHC can be used for a range of initial conditions. We use model predictive control (MPC) to bring the object and end-effector to rest at a desired location after contact is made. The approach is validated in simulation.

Abstract: In data-driven predictive maintenance (PdM), feature extraction is usually treated as fixed preprocessing: a descriptor set is chosen once and reused while the downstream model or forecasting horizon changes. This paper isolates the representation-learning stage and presents a quantile-led feature-extraction framework based on a dual-stage MLP-QRNN hierarchy. QRNN1 learns a broad ten-quantile conditional distribution for each sensor channel, while skip-connected QRNN2 refines a retained mid-tail quantile set into compact, channel-resolved, distribution-aware features. A fixed thirteen-pipeline ablation spans 1-hour, 70-hour, and 30-day regimes across 72 machines in 9 industrial facilities, with the downstream temporal classifier held fixed within each regime. Increasing the retained mid-tail set from two to four quantiles improves 30- and 60-minute F1-score, reaching 75.92% and 72.44% with attention enabled. The results also show that representations do not transfer reliably beyond their design horizon unless feature capacity, temporal embedding, activation strategy, and sensor breadth are scaled with the forecasting task. The unmodified short-horizon extractor falls to 42.90% F1 at 70 hours, whereas horizon-conditioned extractors reach 60.38% at 70 hours and 79.97% at 30 days. The framework therefore supports treating PdM feature extraction as a horizon-dependent representational stage rather than fixed preprocessing.