Collision Localization and Classification on the End-Effector of a Cable-Driven Manipulator Applied to EV Auto-Charging Based on DCNN-SVM.

With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle-robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.

Research on Fast Recognition and Localization of an Electric Vehicle Charging Port Based on a Cluster Template Matching Algorithm.

With the gradual maturity of driverless and automatic parking technologies, electric vehicle charging has been gradually developing in the direction of automation. However, the pose calculation of the charging port (CP) is an important part of realizing automatic charging, and it represents a problem that needs to be solved urgently. To address this problem, this paper proposes a set of efficient and accurate methods for determining the pose of an electric vehicle CP, which mainly includes the search and aiming phases. In the search phase, the feature circle algorithm is used to fit the ellipse information to obtain the pixel coordinates of the feature point. In the aiming phase, contour matching and logarithmic evaluation indicators are used in the cluster template matching algorithm (CTMA) proposed in this paper to obtain the matching position. Based on the image deformation rate and zoom rates, a matching template is established to realize the fast and accurate matching of textureless circular features and complex light fields. The EPnP algorithm is employed to obtain the pose information, and an AUBO-i5 robot is used to complete the charging gun insertion. The results show that the average CP positioning errors (x, y, z, Rx, Ry, and Rz) of the proposed algorithm are 0.65 mm, 0.84 mm, 1.24 mm, 1.11 degrees, 0.95 degrees, and 0.55 degrees. Further, the efficiency of the positioning method is improved by 510.4% and the comprehensive plug-in success rate is 95%. Therefore, the proposed CTMA in this paper can efficiently and accurately identify the CP while meeting the actual plug-in requirements.

Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model.

In this study, the effect of maturity variation on the prediction of the soluble solids content (SSC) and firmness of apples was determined using visible and near-infrared spectroscopy. In 2018, 520 apples from six ripening stages were collected. The single maturity model and multi-maturity model of SSC and firmness were established using partial least-squares regression. Apples at the same and different maturity stages were used to verify the developed model. Whereas the single maturity model was affected by maturity variation, the multi-maturity model could accurately predict the SSC and firmness of apples at different maturity stages. The multi-maturity model developed based on six maturity calibration sets had the best predictive performance. The root mean square error of prediction (RMSEP) of SSC and firmness was 0.614-0.802 °Brix and 0.402-0.650 kg/cm2, respectively. The long-term performance of the optimal multi-maturity model was evaluated using validation sets. The predictive performance was decreased and the RMSEP increased when the model was used to predict the SSC and firmness of apples in different seasons. The predictive performance of the model was improved after slope/bias (S/B) correction, and the RMSEP of SSC and firmness decreased to 0.405-0.587°Brix and 0.518-0.628 kg/cm2 respectively. Overall, the multi-maturity model eliminated the effect of maturity variation, and the multi-maturity model coupled with S/B correction permitted the rapid and accurate detection of the SSC and firmness of apples at different maturity stages and in different seasons.

Robust Adaptive Control of Fully Constrained Cable-Driven Serial Manipulator with Multi-Segment Cables Using Cable Tension Sensor Measurements.

The structure of the cable-driven serial manipulator (CDSM) is more complex than that of the cable-driven parallel manipulator (CDPM), resulting in higher model complexity and stronger structural and parametric uncertainties. These drawbacks challenge the stable trajectory-tracking control of a CDSM. To circumvent these drawbacks, this paper proposes a robust adaptive controller for an n-degree-of-freedom (DOF) CDSM actuated by m cables. First, two high-level controllers are designed to track the joint trajectory under two scenarios, namely known and unknown upper bounds of uncertainties. The controllers include an adaptive feedforward term based on inverse dynamics and a robust control term compensating for the uncertainties. Second, the independence of control gains from the upper bound of uncertainties and the inclusion of the joint viscous friction coefficient into the dynamic parameter vector are realised. Then, a low-level controller is designed for the task of tracking the cable tension trajectory. The system stability is analysed using the Lyapunov method. Finally, the validity and effectiveness of the proposed controllers are verified by experimenting with a three-DOF six-cable CDSM. In addition, a comparative experiment with the classical proportional-integral-derivative (PID) controller is carried out.

Transcriptomic and Metabolic Analyses Provide New Insights into the Apple Fruit Quality Decline during Long-Term Cold Storage.

Long-term low-temperature conditioning (LT-LTC) decreases apple fruit quality, but the underlying physiological and molecular basis is relatively uncharacterized. We identified 12 clusters of differentially expressed genes (DEGs) involved in multiple biological processes (i.e., sugar, malic acid, fatty acid, lipid, complex phytohormone, and stress-response pathways). The expression levels of genes in sugar pathways were correlated with decreasing starch levels during LT-LTC. Specifically, starch-synthesis-related genes (e.g., BE, SBE, and GBSS genes) exhibited downregulated expression, whereas sucrose-metabolism-related gene expression levels were up- or downregulated. The expression levels of genes in the malic acid pathway (ALMT9, AATP1, and AHA2) were upregulated, as well as the content of malic acid in apple fruit during LT-LTC. A total of 151 metabolites, mainly related to amino acids and their isoforms, amines, organic acids, fatty acids, sugars, and polyols, were identified during LT-LTC. Additionally, 35 organic-acid-related metabolites grouped into three clusters, I (3), II (22), and III (10), increased in abundance during LT-LTC. Multiple phytohormones regulated the apple fruit chilling injury response. The ethylene (ET) and abscisic acid (ABA) levels increased at CS2 and CS3, and jasmonate (JA) levels also increased during LT-LTC. Furthermore, the expression levels of genes involved in ET, ABA, and JA synthesis and response pathways were upregulated. Finally, some key transcription factor genes (MYB, bHLH, ERF, NAC, and bZIP genes) related to the apple fruit cold acclimation response were differentially expressed. Our results suggest that the multilayered mechanism underlying apple fruit deterioration during LT-LTC is a complex, transcriptionally regulated process involving cell structures, sugars, lipids, hormones, and transcription factors.