Xiaofeng Yue’s research while affiliated with Changchun University of Technology and other places

For the challenges of low learning efficiency, slow convergence speed and slow inference speed in robot path planning. This paper proposes an improved deep reinforcement learning algorithm for robot path planning. Firstly, the Dueling DQN network architecture is employed, combined with a priority experience replay strategy, to more effectively learn from and utilize experience data. Secondly, the mobility space of the robot is expanded, enhancing the diversity and flexibility of the action space. Additionally, in the action selection process, the Artificial Potential Field (APF) algorithm is introduced to intervene in the action selection with a certain probability, thereby accelerating the convergence process of the network. Simultaneously, the $\varepsilon$ -greedy strategy is employed to balance exploration and exploitation, facilitating better exploration of the environment and utilization of existing knowledge. Furthermore, this paper devises novel composite reward functions that comprehensively integrate multiple reward mechanisms to enhance the convergence performance of the algorithm and the quality of path planning. Finally, the effectiveness and superiority of the proposed method are validated through detailed comparative simulations. Compared to traditional DQN algorithms, Double DQN, and Double DQN with the APF strategy, the method proposed in this paper demonstrates higher learning efficiency and faster convergence speed, enabling more effective planning of shorter paths.

The application of robot technology in the automatic transportation process of packaging bags is becoming increasingly common. Point cloud registration is the key to applying industrial robots to automatic transportation systems. However, current point cloud registration models cannot effectively solve the registration of deformed targets like packaging bags. In this study, a new point cloud registration network, DCDNet-Att, is proposed, which uses a variable weight dynamic graph convolution module to extract point cloud features. A feature interaction module is used to extract common features between the source point cloud and the template point cloud. The same geometric features between the two pairs of point clouds are strengthened through a bottleneck module. A channel attention model is used to obtain the channel attention weights. The attention weight of each spatial position is calculated, and a rotation translation structure is used to sequentially obtain quaternions and translation vectors. A feature fitting loss function is used to constrain the parameters of the neural network model to have a larger receptive field. Compared with seven methods, including the ICP algorithm, GO-ICP algorithm, and FGR algorithm, the proposed method had rotation errors (MAE, RMSE, and Error of 1.458, 2.541, and 1.024 in the ModelNet40 dataset, respectively) and translation errors (MAE, RMSE, and Error of 0.0048, 0.0114, and 0.0174, respectively). When registering the ModelNet40 dataset with Gaussian noise, the rotation errors (MAE, RMSE, and Error) were 2.028, 3.437, and 2.478, respectively, and the translation errors (MAE, RMSE, and Error) were 0.0107, 0.0327, and 0.0285, respectively. The experimental results were superior to those of the other methods, and the model was effective at registering packaging bag point clouds.

In recent years, transfer learning (TL) approaches have seen extensive application in diagnosing bearing faults due to their exceptional performance. However, mechanical noise, equipment aging, and wear lead to notable disparities and differences in the multi-level feature distributions across the source and target domain signals. The issue is addressed by proposing a TL model based on a texture loss strategy and nuclear norm regularization method. First, a feature-enhanced network is designed, which significantly improves the ability to capture local details and long-range dependencies by combining a multi-scale feature extraction module with a dilated residual module. Next, a texture loss strategy is proposed to align multi-scale features across domains by minimizing the Gram matrix of signal features. Finally, a nuclear norm regularization method is proposed to perform low-rank approximation on the signal matrix, facilitating the extraction of more robust feature data and mitigating the risk of overfitting. The experimental results demonstrate that the proposed method achieved an average accuracy of 98.58% on the University of Ottawa bearing fault dataset and 98.11% on the Jiangnan University bearing dataset, surpassing eight other algorithms in bearing fault diagnosis.

With the development of 3D matching technology, point cloud registration (PCR) based on corresponding points has received increasing attention in the field of computer vision. Unfortunately, 3D keypoint technology inevitably produces a large number of outliers. To solve the problems of poor stability, low efficiency and the high number of iterations required to calculate the accepted solution of random sampling consistency (RANSAC) and its variants under a high outlier rate, a streamlined progressive sample consensus (SPROSAC) algorithm is proposed in this paper. SPROSAC is an improved estimator of progressive sample consensus that guides the sampling process by increasing the use of 3D point cloud surface information and optimizes the model verification process based on registration error decision acceptance. Compared to classic RANSAC-family algorithms, SPROSAC has a greater probability of obtaining an accepted solution more quickly. The experiments demonstrate that SPROSAC achieves significantly smaller and more stable registration errors with fewer iterations across three datasets. In the performance experiments based on evaluation metrics such as recall, 1-precision, and F1 score for inlier classification, SPROSAC demonstrates the best performance across the three datasets, with outlier rates exceeding 95%. Furthermore, we propose a coarse–fine PCR algorithm based on SPROSAC and ICP to address the issues of high initialization requirements, susceptibility to local optima, and low efficiency in traditional ICP algorithms. The experimental results of coarse–fine registration show that our algorithm provides initial values for the ICP, which can reduce the number of iterations of the ICP by 50%, 64.4%, and 57.4%.

Cuckoo search (CS) algorithm is a classical swarm intelligence algorithm widely used in a variety of engineering optimization problems. However, its search accuracy and convergence speed still have a lot of room for improvement. In this paper, an improved version of the CS algorithm based on intelligent perception strategy, adaptive invasive weed optimization (AIWO), and elite cross strategy, called IIC-CS is proposed. Firstly, the intelligent perception strategy can update the value according to the searching state. Moreover, the CS is hybridized with the AIWO to improve the searching performance of the algorithm. Additionally, the elite cross strategy is employed to enhance the exploration capability and exploitation capability of the algorithm. Combining the improvements of these three methods, the performance of the CS algorithm is significantly improved. Meanwhile, 23 classical benchmark functions, some CEC2014 and CEC2018 benchmark functions are used to test the search accuracy and convergence rate of the IIC-CS. Furthermore, some classical or state-of-the-art algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), bat algorithm (BA), ant lion optimizer (ALO) and cuckoo search (CS) algorithm, invasive weed optimization (IWO), integrated cuckoo search optimizer (ICSO) and improved island cuckoo search (iCSPM2) are used to make comparisons. Through the statistical results of the experiments, we find that the IIC-CS algorithm can achieve better results on most benchmark functions compared to other algorithms, thus demonstrating the effectiveness of the improvements and the superiority of the IIC-CS algorithm.

Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.