ABSTRACT Real‐time semantic segmentation is crucial for applications including autonomous driving and augmented reality. While current real‐time semantic segmentation methods achieve a balance between accuracy and speed, an adequate capture of boundary details remains a challenge for many models. Furthermore, as deep learning networks become increasingly complex, certain approaches encounter challenges, including excessive computational overhead and numerous parameters when capturing multi‐scale contextual features. To address these limitations, the boundary‐focused and multi‐scale context fusion network (BFMSNet) is proposed, a lightweight real‐time semantic segmentation model that enhances boundary perception and contextual understanding. A boundary refinement module is designed, which utilizes multi‐level feature fusion and a gating mechanism to precisely capture edge details in complex scenes and achieve pixel‐level boundary alignment and optimization. Furthermore, a hybrid boundary loss is introduced, combining region and boundary supervision signals to effectively guide the network's focus on challenging regions, thereby improving training stability and segmentation accuracy. To reduce model complexity, a lightweight multi‐scale fusion module is implemented based on the multi‐scale frequency‐domain characteristics of wavelet convolution. This module balances context information extraction and computational efficiency, reducing parameters while maintaining feature representation. Experimental results on the Cityscapes and CamVid datasets demonstrate that BFMSNet achieves mIoU of 78.53% and 76.24%, while maintaining real‐time inference speeds of 86.25 FPS and 143.70 FPS, respectively. Preliminary tests indicate that the BFMSNet algorithm effectively balances accuracy and speed requirements.

Accurate trajectory training is a challenging issue of upper-limb rehabilitation robots. This paper presents a novel discrete-time neural network control method to address the problems of system uncertainties and tracking accuracy in repetitive trajectory training. This control method consists of both an adaptive neural network controller and a learning controller. The adaptive neural network controller satisfying persistent excitation condition enables not only stable tracking control, but also accurate learning for closed-loop system dynamics. The learning controller utilizes the learned knowledge to provide high-performance control. In order to examine the effectiveness of the proposed control method, a series of simulation and real-world experiments with system uncertainties were conducted, in comparison of proportion integration differentiation control, sliding mode control and event-triggered adaptive neural control. Results substantiate that the proposed control method can precisely learn the unknown dynamics of human-robot system along the subject-specific reference trajectories, and control the robot to assist the arm for accurate and fast trajectory tracking with small control gains by reutilizing the learned knowledge. This control method may play a role in accurate trajectory training for upper-limb rehabilitation robots. Note to Practitioners—This work is motivated by the practical requirements of rehabilitation robots in repetitive motor training. Trajectory tracking is a fundamental but efficient training mode of rehabilitation robots. However, uncertainty and nonlinearity of the human-robot system dynamics may increase the difficulty of controlling the robots for accurate, efficient and reliable trajectory tracking training. To this end, this paper proposes a learning-based control method, which could learn the uncertain and nonlinear system dynamics by utilizing an elaborately designed neural network controller and thus achieve superior control performance using the learned knowledge. This control method can be potentially applied in variety of rehabilitation robots, showing advantages for repetitive trajectory training. First, it can accurately mode the uncertain dynamics of human-robot system and achieve personalized rehabilitation. Second, it does not need any parameter adaptation in the similar repeated motions, and can be more easily designed with digital implementations, thereby achieving better performance in the aspects of time saving. Third, it can ensure the tracking accuracy of the rehabilitation robot for rehabilitation efficiency and avoid secondary injury.

This study investigates a multitrajectory tracking control strategy for an unknown 2-DOF helicopter system, integrating deterministic learning (DL) and reinforcement learning (RL). Initially, DL theory is applied to identify the local unknown dynamics of a 2-DOF helicopter system using radial basis function neural networks (RBFNNs). Subsequently, the identified dynamic knowledge is expressed and stored using constant RBFNNs. To mitigate the issue of partial knowledge failure due to deviations between the actual and learned trajectories, we introduce a RL framework for dynamic compensation. Finally, a composite control strategy incorporating both nominal and auxiliary components is designed to achieve multitrajectory tracking control. The stability of the closed-loop system is analyzed and demonstrated using the Lyapunov direct method. The simulation and experimental results demonstrate the effectiveness of the proposed control strategy.

Rapid dynamical pattern recognition based on the deterministic learning method (DLM-based RDPR) aims to rapidly recognize the most similar dynamical pattern pair from perspectives of differences in inherent system dynamics. The basic mechanism is to use available recognition errors to reflect the differences in the dynamics of dynamical pattern pairs and then to make a decision based on a minimal recognition error (MRE) principle. This article focuses on providing a rigorous theoretical analysis of the MRE principle in DLM-based RDPR under the sampled-data framework. Specifically, we seek a unified methodology from the similarity definition to the measure implementation and then to derive general sufficient conditions and necessary conditions for the MRE principle. The main idea is to: 1) from the average signal energy aspect, define a time-dependent dynamics-based similarity in dynamical pattern pairs and reestablish the measure of recognition errors generated from the DLM-based RDPR; 2) introduce the energy-based Lyapunov method to establish the interrelation between the dynamical distance and the recognition error; and 3) derive sufficient conditions and necessary conditions from two directions of the interrelation. The proposed conditions distinguish themselves from virtually all of the existing DLM-based RDPR works with only sufficient conditions in the sense that it is shown in a rigorous analysis that under what conditions, the pattern pair recognized based on the MRE principle is indeed the most similar one. Therefore, the proposed work makes the DLM-based RDPR possess good interpretability and provides strong theoretical guidance in engineering applications.

The effective fine-grained identification of citrus varieties plays a vital role in the differential production management of citrus orchards. To our knowledge, there are few studies and publicly available datasets on fine-grained identification of citrus varieties. In this study, we propose Wavelet Channel Attention Network (WCANet) to solve the problem of fine-grained visual classification of citrus varieties and create a Citrus Variety Dataset (CVD) consisting of tree canopy images. WCANet combines global average pooling to extract global features and wavelet transform to capture local features, which greatly improves the capability of channel attention modules for multi-scale feature extraction. Experimental results demonstrate that the WCANet outperforms the state-of-the-art confidence estimation approaches on various benchmarks. Our code and dataset will be open-sourced at https://github.com/fightero/WCANet.

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://github.com/Star-Robot/chinese-chess-robot.

This article investigates dynamical pattern recognition for a class of univariate time-series data. These data are sampled from the output of dynamical systems with uncertain dynamics. Based on deterministic learning, a rapid recognition approach is presented from the viewpoint of the sample-data observer. It comprises two phases: 1) training and 2) recognition. In the training phase, locally accurate dynamical modeling of the underlying dynamics of training time series can be accomplished by merging a sampled-data observer and radial basis function network (RBFN) identifiers. In the recognition phase, several RBFN-based estimators with non-high-gain designs are constructed. In this case, the stability analysis of the generated estimator error systems will conduce to conduct non-high-gain recognition of a test time series. We demonstrate that these estimator errors can depict dynamics differences between the dynamical patterns of the test and training time-series data. Based on the average$L_{1}$norms of the output errors, a decision-making scheme is developed to generate recognition results rapidly. More concise and relaxed recognition conditions are derived through rigorous analysis to ensure accurate recognition results. Simulation studies exemplify the effectiveness of the presented approach.