Visual-language alignment is crucial for enhancing the domain adaptability of industrial anomaly detection models. However, the existing methods overlook the importance of structured image representation, fail to further distinguish topological differences between anomalies and the inherent textures of products, which reduces the accuracy of semantic matching. To address this problem, we propose a novel industrial anomaly detection model G-Anomaly, to preserve the topological structure of the sample images and further enhance the model’s domain adaptability. We designed Pyramid Graph Transformer as a visual encoder to extract multi-scale visual features, which can directly preserve the structural relationships between different regions of the image, and also optimize the over-smoothing issue present in deep graph networks, thereby retaining the distinguishability of anomalous nodes. Additionally, we design a Multi-level Domain Adapter that ensures semantic consistency of anomalous features across different scales and contexts by performing visual-language matching at various resolutions and levels of abstraction. This enhances the model’s domain adaptability for anomaly detection for a wide range of industrial products. We collect and craft an actual solar panel dataset PV_actual AD, and conduct extensive experiments on the public dataset MVTec AD as well as the actual solar panel dataset PV_actual AD. This has demonstrated that G-Anomaly not only performs well in standard testing environments but also exhibits robustness and domain adaptability for anomaly detection tasks in real-world scenarios.

The problem of autonomous navigation has attracted significant attention from robotics research community in the last few decades. In this paper, we address the problem of low data utilization due to the large amount of episode experience value data. A maximum entropy algorithm based on prioritized experience replay (Learning Good Experience based on Soft Actor-Criti, LGE-SAC) is proposed to quickly reproduce past good experience episodes. As the deep reinforcement learning method is susceptible to failure to plan ahead and explore the target position in a long sequence environment, a deep Residual Soft Actor-Critic (RSAC) is proposed to alleviate this problem. The reinforcement learning policy is fused with the Artificial Potential Field method to improve the generalization ability of the proposed algorithm, thus improving robot adaptation in new test environments. In order to validate the effectiveness of the proposed algorithm, we conducted simulation experiments in Gazebo simulator environment and real experiments on a Turtlebot3 robot equipped with LiDAR sensor. Simulation and experiment results show that the proposed algorithm effectively avoids obstacles and succeeds in reaching the goal compared to other obstacle avoidance algorithms. In comparison with the Artificial Potential Field method, the planning success rate of the proposed RSAC algorithm in the test environment is increased by 30%, and at the same time, the number of planning steps is reduced by half, and the generalization ability is improved.

Traditional visual-inertial Simultaneous Localization and Mapping (SLAM) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues, such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intrarobot and interrobot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance.

The integration of cloud computing and edge computing is an effective way to achieve global consistent and real-time multi-robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited computing, communication and storage capacity of terminal equipment. However, limited bandwidth and extremely long communication links between terminal devices and the cloud result in serious performance degradation of multi-robot SLAM systems. To reduce the computational cost of feature tracking and improve the real-time performance of the robot, a lightweight SLAM method of optical flow tracking based on pyramid IMU prediction is proposed. On this basis, a centralized multi-robot SLAM system based on a robot-edge-cloud layered architecture is proposed to realize real-time collaborative SLAM. It avoids the problems of limited on-board computing resources and low execution efficiency of single robot. In this framework, only the feature points and keyframe descriptors are transmitted and lossless encoding and compression are carried out to realize real-time remote information transmission with limited bandwidth resources. This design reduces the actual bandwidth occupied in the process of data transmission, and does not cause the loss of SLAM accuracy caused by data compression. Through experimental verification on the EuRoC dataset, compared with the current most advanced local feature compression method, our method can achieve lower data volume feature transmission, and compared with the current advanced centralized multi-robot SLAM scheme, it can achieve the same or better positioning accuracy under low computational load.Note to Practitioners—The purpose of this paper is to reduce the communication load of a Cloud-Edge-Robot system by compressing and transmitting of keyframes and non-keyframes, respectively, which is suitable for a multi-robot SLAM system and can realize multi-robot joint localization and sparse map reconstruction under efficient communication. Currently, remote SLAM or centralized multi-robot SLAM is usually implemented by transferring the whole image or the features and descriptors of the image. In this paper, lightweight SLAM optical flow tracking based on pyramid IMU prediction is implemented to track non-keyframes. At the edge server, tracking between non-keyframes is realized only by transmitting keypoints. For keyframes, the pose estimation is realized by transmitting compressed features and descriptors. Multi-robot localization and map fusion are realized in the cloud through key frame feature information. Experiments on public datasets show that this method is feasible and can achieve high-precision joint positioning with a low amount of transmitted data. In future studies, we will apply this framework to more real-world systems, while achieving rich, accurate map fusion with more advanced features.

Point cloud segmentation is one of the challenging areas due to its disorder and irregularity. Currently, a lot of work utilising Transformer instead of conventional convolution methods has been proposed, which can well cope with these difficulties and is suitable for point cloud segmentation tasks. However, most existing Transformer methods extract global or local features in isolation, failing to obtain rich contextual information. In this letter, a cross-scale Transformer network for feature extration is proposed. Multi-level contextual information is captured appllying FPS algorithm. Integrated with point cloud convolution method, achieving excellent segmentation performance. Extensive experiments on SemanticKITTI dataset demonstrate the superior performance of the proposed method on mIoU.

Visual simultaneous localization and mapping (VSLAM) is a crucial technology in intelligent vehicles that relies on either explicit or implicit representations. Explicit methods are prevalent in real-time systems, offer precise geometric control, and are easy to visualize. However, they struggle with complex, dynamic environments and require high storage capacity. On the other hand, implicit techniques excel in handling intricate, changing shapes due to their compact representation and inference ability while requiring more complex display and rendering processes. A combination of both types of representations could significantly enhance the performance of VSLAM, but the cross-data association method for standalone explicit and implicit representations is still lacking. To this end, this paper proposes a data association scheme that bridges the gap between explicit and implicit representations by individually modeling the uncertainties in each representation. Our approach features a multi-level feature selection process tailored for data association. It initially extracts coarse-level features during explicit representation generation based on Bayesian estimation and refines them using the implicit representation based on ray sampling, which enhances robustness while reducing rendering costs. We rigorously evaluated our proposed methodology against current state-of-the-art approaches using public datasets and real robot scenes. The results show that our coarse-to-fine feature selection method outperforms existing techniques both quantitatively and qualitatively, suggesting its potential to significantly boost the contemporary VSLAM system performance.

This article addresses the delay-dependent Takagi–Sugeno (T–S) fuzzy state feedback control and exponential admissibility analysis for a class of T–S fuzzy singular uncertain systems. First, the T–S fuzzy model is employed to approximate the singular uncertain system with time-varying delay, saturation input, and unmatched disturbance. Second, the delay-dependent T–S fuzzy state feedback controller is designed by employing the T–S fuzzy model. Third, the free-weighting matrices and delay-dependent Lyapunov–Krasovskii functional with multiple integral terms are employed to derive the delay-dependent exponential admissibility conditions and prescribed H-infinity performance is guaranteed. Compared with previous works, the delay-dependent T–S fuzzy state feedback controller is designed for the T–S fuzzy singular uncertain system to relax system design conditions. The convex hull lemma is employed to convert the closed-loop system with saturation input into the closed-loop system without saturation input to enhance controller design flexibility. The Schur complement lemma and Gronwall Bellman lemma are employed to derive the less conservative delay-dependent stability conditions for determining controller gain matrices. The exact invariant set with less conservativeness is employed to convert the controller design problem into linear matrix inequalities (LMIs) optimization constraints to reduce computation complexity of solving LMIs. Finally, simulation examples are presented to show the effectiveness of the proposed methods.

Keypoint matching is an important operation in computer vision and its applications such as visual simultaneous localization and mapping (SLAM) in robotics. This matching operation heavily depends on the descriptors of the keypoints, and it must be performed reliably when images undergo conditional changes such as those in illumination and viewpoint. In this paper, a descriptor fusion model (DFM) is proposed to create a robust keypoint descriptor by fusing CNN-based descriptors using autoencoders. Our DFM architecture can be adapted to either trained or pre-trained CNN models. Based on the performance of existing CNN descriptors, we choose HardNet and DenseNet169 as representatives of trained and pre-trained descriptors. Our proposed DFM is evaluated on the latest benchmark datasets in computer vision with challenging conditional changes. The experimental results show that DFM is able to achieve state-of-the-art performance, with the mean mAP that is 6.45% and 6.53% higher than HardNet and DenseNet169, respectively.

In this article, the stability analysis and control synthesis of interval type-2 (IT2) polynomial-fuzzy-model-based networked control systems are investigated under the event-triggered control framework. The nonlinear dynamics in the plant is efficiently represented by an IT2 polynomial fuzzy model that the IT2 membership functions are utilized to capture the uncertainties in the plant. An event-triggered IT2 polynomial fuzzy controller is then designed to stabilize the nonlinear model subject to uncertainties. The stability conditions of the closed-loop control system are summarized in the form of sum-of-squares. Under the imperfectly premise matching (IPM) concept, the membership-function-dependent (MFD) approach is applied to endow the polynomial fuzzy controllers with more flexibility in terms of number of rules and premise membership functions. In the MFD approach under the IPM concept, both the number of rules and the shape of membership functions in the fuzzy models and controllers can be different. Also, the information of IT2 membership functions of the polynomial fuzzy model and controller is considered and adopted to further relax the stability conditions. Furthermore, the intrinsic mismatched issue of the premise variables of the fuzzy model and controllers due to the event-triggering mechanism is handled by the MFD approach. A detailed simulation example is provided to verify the effectiveness of the proposed event-based control strategy.

Energy efficiency has become an increasingly important aspect of wireless communications. Coordinated multipoint (CoMP) processing may be considered as a promising approach to improve the energy efficiency, extend the cell coverage, and increase the communication capacity in wireless cellular networks. However, the CoMP communication systems may require a large amount of channel state information (CSI) feedbacks which would be constrained by the wireless backhaul networks. This paper proposes a control theoretical approach to improve the energy efficiency in cooperative wireless cellular networks with CoMP communications. In particular, we propose a stochastic predictive control algorithm to achieve energy-efficient CoMP transmissions through optimal base station grouping. Moreover, since the channel state information in CoMP is often outdated/lost due to the feedback delays in the backhaul network, our proposed approach is based on a new discrete time predictive function control model to mitigate the impacts of packet delay or loss of channel state information. Simulation results show the effectiveness of the proposed scheme.

Coordinated multipoint (CoMP) communication is a new method that improves energy efficiency, extends cell coverage, and increases capacity in cellular networks. However, since channel state information from the involved cells is needed in CoMP systems, practical systems that employ CoMP techniques suffer from constraints imposed by the backhaul network. In this paper, we take a novel approach to study the energy efficiency issues in cooperative wireless cellular networks with CoMP communications. Specifically, to derive the base station grouping decisions for energy-efficient transmissions in CoMP, we use stochastic predictive control algorithm. Simulation results are presented to show the effectiveness of the proposed scheme.

This paper addresses a virtual force field between mobile robots and obstacles to keep them away with a desired distance. An online learning method of hybrid force/position control is proposed for obstacle avoidance in a robot environment. An Elman neural network is proposed to compensate the effect of uncertainties between the dynamic robot model and the obstacles. Moreover, this paper uses an Elman fuzzy adaptive controller to adjust the exact distance between the robot and the obstacles. The effectiveness of the proposed method is demonstrated by simulation examples.