Peijuan Li

In orchard harvesting, complex occlusions, dense obstacles, and limited workspace make manipulator motion planning an autonomous decision-making problem in unstructured environments, requiring collision avoidance while balancing efficiency and trajectory smoothness. However, traditional Artificial Potential Field (APF) and Rapidly-Exploring Random Tree (RRT) algorithms have limitations in meeting harvesting demands. To address this, an enhanced APF-RRT algorithm is proposed via three improvements: potential field modeling, sampling expansion, and path optimization—an exponential smooth attractive field and a non-linear repulsive field to improve potential-field continuity and guidance stability; an obstacle-distance-based dynamic expansion strategy for rapid search and precise avoidance, reducing redundant nodes; and cubic B-spline smoothing to enhance path quality and motion stability. Validation was conducted under laboratory-simulated orchard obstacle conditions rather than in actual orchards. Compared with traditional RRT, the enhanced algorithm reduces planning time, path length, and sampling nodes by 83.55%, 18.04%, and 81.20% in 2D and by 89.77%, 20.10%, and 88.74% in 3D. Further manipulator experiments show reductions in sparse scenarios of 20.38%/21.22%, 19.71%/20.02%, and 18.46%/18.74% (single-/multi-category) and in dense scenarios of 21.62%/18.57%, 21.20%/19.65%, and 20.36%/18.74%. Results demonstrate improved planning efficiency, shorter paths, and enhanced motion stability, validating the algorithm’s generalization and adaptability.

The triaxis rotational inertial navigation system (TRINS) is crucial for underwater long-duration, high-precision navigation and positioning. The accuracy of initial alignment directly affects the position error in the navigation of the TRINS. Traditional alignment method include coarse alignment and fine alignment. In fine alignment, only bias errors are considered. However, conducting the initial alignment while the ship is in a moored state can lead to more IMU errors, thereby reducing the alignment accuracy. Therefore, a scheme for the simultaneous self-calibration and self-alignment of TRINS under mooring is proposed, in order to suppress the impact of additional IMU errors excited by sway disturbance on the alignment. A full-parameter IMU error model including the quadratic error of accelerometers and lever arm errors is established. And a self-rotation calibration trajectory scheme with 36 position is designed. In order to reduce the calibration time and thereby enhance navigation efficiency, an iterative Kalman filtering method is employed. The entire process takes 30 minutes. Simulations and experiments both utilize the traditional 19-position method for comparison validate the efficacy of the proposed method. Furthermore, navigation testing show that errors in position and velocity are significantly decreased by calibration errors correction, demonstrating that the proposed approach could improve TRINS’ positioning accuracy.

Angle-of-arrival (AOA) measurements are often used in underwater acoustical localization. Different from the traditional AOA model based on azimuth and elevation measurements, the AOA model studied in this paper uses bearing measurements. It is also often used in the Ultra-Short Baseline system (USBL). However, traditional acoustical localization needs additional range information. If the range information is unavailable, the closed-form solution is difficult to obtain only with bearing measurements. Thus, a localization closed-form solution using only bearing measurements is explored in this article. A pseudo-linear measurement model between the source position and the bearing measurements is derived, and considering the nonlinear relationship of the parameters, a weighted least-squares optimization equation based on multiple constraints is established. Different from the traditional two-step least-squares method, the semidefinite programming (SDP) method is designed to obtain the initial solution, and then a bias compensation method is proposed to further minimize localization errors based on the SDP result. Numerical simulations show that the performance of the proposed method can achieve Cramer–Rao lower bound (CRLB) accuracy. The field test also proves that the proposed method can locate the source position without range measurements and obtain the highest positioning accuracy.

In current epilepsy disease research, accurate identification of epilepsy electroencephalogram (EEG) signals is crucial for improving diagnostic efficiency and developing personalized treatment plans. This study proposes an innovative epilepsy recognition model, MAC, which combines the unique advantages of a multilayer perceptron (MLP), a self-attention mechanism and the cosine distance. This model uses a MLP as the basic model and effectively reduces individual differences among epilepsy patients through its superior linear fitting ability. To more accurately measure the difference between two EEG signals, we introduced the cosine distance as a new feature metric. This metric enhances the performance of epilepsy EEG classification by using the cosine value of the angle in vector space to precisely assess the difference between two individuals. In addition, we introduced a self-attention mechanism into the model to enhance the impact of various factors on the final EEG data. Our experiments employed the EEG database of the Epilepsy Research Center of the University of Bonn. Through comparative experiments, it was proven that the proposed MAC model achieved significant improvement in performance on the epilepsy EEG signal recognition task. This study fills the existing research gap in the field of epilepsy identification and provides a powerful tool for the accurate diagnosis of epilepsy diseases in the future. We believe that the introduction of the MAC model will promote new breakthroughs in epilepsy EEG signal recognition and lay a solid foundation for the development of related fields. This research provides an important theoretical and practical reference for advancing the field of epilepsy identification.

At present, the core of lidar data registration algorithms depends on search correspondence, which has become the core factor limiting the performance of this kind of algorithm. For point-based algorithms, the data coincidence rate is too low, and for line-based algorithms, the method of searching the correspondence is too complex and unstable. In this paper, a laser radar data registration algorithm based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering is proposed, which avoids the search and establishment of the corresponding relationship. Firstly, a ring band filter is designed to process the outliers with noise in a point cloud. Then, the adaptive threshold is used to extract the line segment features in the laser radar point cloud. For the point cloud to be registered, a DBSCAN density clustering algorithm is used to obtain the key clusters of the rotation angle and translation matrix. In order to evaluate the similarity of the two frames of the point cloud in the key clusters after data registration, a kernel density estimation method is proposed to describe the registered point cloud, and K-L divergence is used to find the optimal value in the key clusters. The experimental results show that the proposed algorithm avoids the direct search of the correspondence between points or lines in complex scenes with many outliers in laser point clouds, which can effectively improve the robustness of the algorithm and suppress the influence of outliers on the algorithm. The relative error between the registration result and the actual value is within 10%, and the accuracy is better than the ICP algorithm.

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.

The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real‐time localization and map‐matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative calculation method with high computational complexity and a large amount of computation. Furthermore, due to the dependence of the fixed separation threshold, the algorithms for extracting the linear features of laser radar data are typically undersegment and oversegment. As a result, this paper proposes a radar linear feature fitting algorithm that combines adaptive clustering and corner detection operators. First, bilateral filtering is used to reduce noise and remove invalid data points. Second, the LiDAR data points are classified using adaptive threshold clustering of distance and density. The corner detection operator is applied to the classified data points to determine all possible corners then. Finally, the least square method is used to linearly fit each class and the identified corners within each class. The simulation and experimental results demonstrate that this method avoids the influence of noise points and a fixed segmentation threshold on corner point extraction effectively. The standard variance of length is 9.41 × 10⁻⁵m² for corner feature extraction and localization in the dataset Cartographer ROS 2D Laser SLAM at Deutsches Museum. When compared to PDBS (point distance based methods) and IEPF (iterative end point fit), only about half the time is used, the accuracy of partition processing is improved by 11.6%, and the accuracy of corner detection is improved by 20.1%. The proposed algorithm can extract the corner features of data frames and linear positioning through experimental verification accurately. The features of the laser scan data that fit are more realistic. It has higher calculation efficiency and position accuracy. It ensures real‐time mobile robot map construction and is appropriate for autonomous robot map algorithms developed in embedded systems.

Polypyrimidine tract binding protein 3 (PTBP3) plays a critical role in post-transcriptional regulation. The role of PTBP3 in various human tumours was explored and analysed in this study based on the Cancer Genome Atlas and Gene Expression Omnibus datasets. PTBP3 was highly expressed in most tumours, such as breast invasive carcinoma, colon adenocarcinoma and hepatocellular carcinoma. PTBP3 overexpression generally predicts poor overall survival and disease-free survival in patients with adrenocortical carcinoma, lung squamous cell carcinoma, and pancreatic adenocarcinoma. However, low PTBP3 expression predicts poor prognosis in kidney renal clear cell carcinoma. We also explored PTBP3 genetic alterations in different tumour tissues. The result found that the frequency of PTBP3 alteration (>4%) was the highest in uterine tumours with “mutation” as the primary type. Furthermore, we found a significant correlation between PTBP3 expression and tumour mutational burden and microsatellite instability in various human tumours, and found that PTBP3 expression was positively correlated with TMB in ACC, STAD, PAAD, LUAD, and SARC. Two enhanced phosphorylation levels of S30 and S426 in colon cancer, ovarian cancer, and uterine corpus endometrial carcinoma were found. Further analysis indicated that PTBP3 expression was positively correlated with the cancer-associated fibroblasts for most tumour types. This study also found a relationship between immune checkpoints and N6-methyladenosine-related markers and PTBP3 expression. Moreover, the “mRNA surveillance pathway” and “RNA degradation” were involved in the functional mechanisms of PTBP3. These results provide new insights for molecular studies, and integrative analysis provided a framework for determining the predictive, prognostic, and therapeutic relevance of PTBP3 in cancer patients.

Considering the drawbacks that GPS signal is susceptible to obstacles and TAN becomes useless in some area when without any terrain data or with a featureless terrain field, to realize long-distance and high-precision navigation, a navigation system based on SINS/GPS/TAN/EOAN is presented. When GPS signal is available, GPS is used to correct errors of SINS; when GPS is unavailable, a terrain selection method based on the entropy weighted gray relational decision-making method is use to distinguish terrain into matchable areas and unmatchable areas; then, for the matchable areas, TAN is used to correct errors of SINS, for the unmatchable areas, EOAN is used to correct errors of SINS. The principles of SINS, GPS, TAN, and EOAN are analyzed, the mathematic models of SINS/GPS, SINS/TAN, and SINS/EOAN are constructed, and finally the federated Kalman filter is used to fuse navigation information. Simulation results show that the trajectory of SINS/GPS/TAN/EOAN is close to the ideal one in both matchable area or unmatchable area and whose navigation errors are obviously reduced, which is important for the realization of long-time and high-precision positioning.