A Closed-Form Solution for the Inverse Kinematics of the 2n-DOF Hyper-Redundant Manipulator Based on General Spherical Joint

This paper presents a closed-form inverse kinematics solution for the 2n-degree of freedom (DOF) hyper-redundant serial manipulator with n identical universal joints (UJs). The proposed algorithm is based on a novel concept named as general spherical joint (GSJ). In this work, these universal joints are modeled as general spherical joints through introducing a virtual revolution between two adjacent universal joints. This virtual revolution acts as the third revolute DOF of the general spherical joint. Remarkably, the proposed general spherical joint can also realize the decoupling of position and orientation just as the spherical wrist. Further, based on this, the universal joint angles can be solved if all of the positions of the general spherical joints are known. The position of a general spherical joint can be determined by using three distances between this unknown general spherical joint and another three known ones. Finally, a closed-form solution for the whole manipulator is solved by applying the inverse kinematics of single general spherical joint section using these positions. Simulations are developed to verify the validity of the proposed closed-form inverse kinematics model

A Closed-Form Solution for the Inverse Kinematics of the 2<i>n</i>-DOF Hyper-Redundant Manipulator Based on General Spherical Joint

This paper presents a closed-form inverse kinematics solution for the 2n-degree of freedom (DOF) hyper-redundant serial manipulator with n identical universal joints (UJs). The proposed algorithm is based on a novel concept named as general spherical joint (GSJ). In this work, these universal joints are modeled as general spherical joints through introducing a virtual revolution between two adjacent universal joints. This virtual revolution acts as the third revolute DOF of the general spherical joint. Remarkably, the proposed general spherical joint can also realize the decoupling of position and orientation just as the spherical wrist. Further, based on this, the universal joint angles can be solved if all of the positions of the general spherical joints are known. The position of a general spherical joint can be determined by using three distances between this unknown general spherical joint and another three known ones. Finally, a closed-form solution for the whole manipulator is solved by applying the inverse kinematics of single general spherical joint section using these positions. Simulations are developed to verify the validity of the proposed closed-form inverse kinematics model

Self-Calibration for the General Cable-Driven Serial Manipulator with Multi-Segment Cables

This paper focuses on the kinematic calibration problem for the general cable-driven serial manipulator (CDSM) with multi-segment cables to improve its motion control accuracy. Firstly, to fully describe the calibration parameters of cables, links, joint positions, and the transmission system, this paper proposes a new cable routing description method named cable-routing configuration struct (CRCS), which provides a complete set of parameters to be calibrated for the proposed self-calibration algorithm. Then, a self-calibration algorithm for CDSM with motor incremental encoders is proposed, which can calibrate the robot at one time only using sufficient measured motor and joint positions. Its premise, the initial cable length, needs to be calibrated. Finally, the parameters of a three-DOF (degree of freedom) six-cable CDSM were described using the CRCS description method, and a comparative experiment was carried out on the same motion controller using the parameters before and after calibration. The experiment results of trajectory tracking error showed that the calibration parameters obtained by the proposed calibration algorithm can significantly improve the motion control accuracy of the three-DOF six-cable CDSM. This verified the correctness and effectiveness of the proposed calibration algorithm

Self-Calibration for the General Cable-Driven Serial Manipulator with Multi-Segment Cables

This paper focuses on the kinematic calibration problem for the general cable-driven serial manipulator (CDSM) with multi-segment cables to improve its motion control accuracy. Firstly, to fully describe the calibration parameters of cables, links, joint positions, and the transmission system, this paper proposes a new cable routing description method named cable-routing configuration struct (CRCS), which provides a complete set of parameters to be calibrated for the proposed self-calibration algorithm. Then, a self-calibration algorithm for CDSM with motor incremental encoders is proposed, which can calibrate the robot at one time only using sufficient measured motor and joint positions. Its premise, the initial cable length, needs to be calibrated. Finally, the parameters of a three-DOF (degree of freedom) six-cable CDSM were described using the CRCS description method, and a comparative experiment was carried out on the same motion controller using the parameters before and after calibration. The experiment results of trajectory tracking error showed that the calibration parameters obtained by the proposed calibration algorithm can significantly improve the motion control accuracy of the three-DOF six-cable CDSM. This verified the correctness and effectiveness of the proposed calibration algorithm

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

Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data

Farmland parcel-based crop classification using satellite data plays an important role in precision agriculture. In this study, a deep-learning-based time-series analysis method employing optical images and synthetic aperture radar (SAR) data is presented for crop classification for cloudy and rainy regions. Central to this method is the spatial-temporal incorporation of high-resolution optical images and multi-temporal SAR data and deep-learning-based time-series analysis. First, a precise farmland parcel map is delineated from high-resolution optical images. Second, pre-processed SAR intensity images are overlaid onto the parcel map to construct time series of crop growth for each parcel. Third, a deep-learning-based (using the long short-term memory, LSTM, network) classifier is employed to learn time-series features of crops and to classify parcels to produce a final classification map. The method was applied to two datasets of high-resolution ZY-3 images and multi-temporal Sentinel-1A SAR data to classify crop types in Hunan and Guizhou of China. The classification results, with an 5.0% improvement in overall accuracy compared to those of traditional methods, illustrate the effectiveness of the proposed framework for parcel-based crop classification for southern China. A further analysis of the relationship between crop calendars and change patterns of time-series intensity indicates that the LSTM model could learn and extract useful features for time-series crop classification

Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN

For safety reasons, in order to ensure that a robot can make a reasonable response after a collision, it is often necessary to localize the collision. The traditional model-based collision localization methods, which are highly dependent on the designed observer, are often only useful for rough localization due to the bias between simulation and real-world application. In contrast, for fine collision localization of small-scale regions, data-driven methods can achieve better results. In order to obtain high localization accuracy, the data required by data-driven methods need to be as comprehensive as possible, and this will greatly increase the cost of data collection. To address this problem, this article is dedicated to developing a data-driven method for zero-shot collision localization based on local region data. In previous work, global region data were used to construct the collision localization model without considering the similarity of the data used for analysis caused by the assembly method of the contact parts. However, when using local region data to build collision localization models, the process is easily affected by similarity, resulting in a decrease in the accuracy of collision localization. To alleviate this situation, a two-stage scheme is implemented in our method to simultaneously isolate the similarity and realize collision localization. Compared with the classical methods, the proposed method achieves significantly improved collision localization accuracy

Research on Identification and Location of Charging Ports of Multiple Electric Vehicles Based on SFLDLC-CBAM-YOLOV7-Tinp-CTMA

With the gradual maturity of autonomous driving and automatic parking technology, electric vehicle charging is moving towards automation. The charging port (CP) location is an important basis for realizing automatic charging. Existing CP identification algorithms are only suitable for a single vehicle model with poor universality. Therefore, this paper proposes a set of methods that can identify the CPs of various vehicle types. The recognition process is divided into a rough positioning stage (RPS) and a precise positioning stage (PPS). In this study, the data sets corresponding to four types of vehicle CPs under different environments are established. In the RPS, the characteristic information of the CP is obtained based on the combination of convolutional block attention module (CBAM) and YOLOV7-tinp, and its position information is calculated using the similar projection relationship. For the PPS, this paper proposes a data enhancement method based on similar feature location to determine the label category (SFLDLC). The CBAM-YOLOV7-tinp is used to identify the feature location information, and the cluster template matching algorithm (CTMA) is used to obtain the accurate feature location and tag type, and the EPnP algorithm is used to calculate the location and posture (LP) information. The results of the LP solution are used to provide the position coordinates of the CP relative to the robot base. Finally, the AUBO-i10 robot is used to complete the experimental test. The corresponding results show that the average positioning errors (x, y, z, rx, ry, and rz) of the CP are 0.64 mm, 0.88 mm, 1.24 mm, 1.19 degrees, 1.00 degrees, and 0.57 degrees, respectively, and the integrated insertion success rate is 94.25%. Therefore, the algorithm proposed in this paper can efficiently and accurately identify and locate various types of CP and meet the actual plugging requirements

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

Potential missed opportunities for diagnosis of lymphoepithelioma-like intrahepatic cholangiocarcinoma: report of a rare case

Lymphoepithelioma-like intrahepatic cholangiocarcinoma (LEL-ICC) is a rare distinctive variant of liver cancer with unique epidemiological and pathological characteristics, including dense lymphocyte infiltration. We herein describe a 67-year-old Chinese man with LEL-ICC. The patient had undergone endoscopic extraction of a bile duct stone 1 month prior. Contrast-enhanced abdominal computed tomography (CT) revealed a 2.5- × 2.5- × 1.5-cm low-density mass located in a covert part of the left lateral segment of the liver. Contrast-enhanced magnetic resonance imaging revealed a hyperintense lesion on T2-weighted and diffusion-weighted images of the left lateral liver, with similar size and signal characteristics in the arterial and portal venous phases. The patient subsequently underwent left lateral laparoscopic hepatectomy. The results of postoperative pathology and immunohistochemistry allowed for the definitive diagnosis. In situ hybridization using an Epstein–Barr virus-encoded RNA probe revealed extensive reactivity in the tumor cell nuclei, supporting a diagnosis of LEL-ICC. The patient was recurrence-free at 12 months postoperatively as shown by CT. A literature review indicated that in middle-aged patients with Epstein–Barr virus infection, a liver mass with a well-defined margin and a combination of hypervascularity and delayed intratumoral enhancement on CT and magnetic resonance imaging may suggest a diagnosis of LEL-ICC