Regular cleaning and maintenance of high-altitude pipes and curved surfaces on high-rise buildings are high-risk tasks for human workers due to the difficulty of working on curved planes. To address such challenge, automated robots are widely used for cleaning buildings with flat walls, but they cannot climb on curved surfaces, limiting their practical applications. This paper proposes a novel biped curved-surface climbing robot (BCCR) with five-degree-of-freedom (5-DOF) motion. The BCCR features adaptive vacuum suction modules that can adhere to both curved and flat surfaces, allowing seamless movement of the BCCR across various surfaces. Each terminal suction module is composed of three small suction cups, which are capable of rotating in all directions to achieve adaptive adhesion on various surfaces. The 5-DOF structure enables the robot to cross obstacles and makes it highly versatile for various cleaning tasks on a wide range of surfaces, including large curved pipes. The mechanism design and analytical modeling of the BCCR are carried out, demonstrating its robust curved-surface climbing capabilities. Moreover, a prototype is fabricated for experimental investigation. The results indicate that the proposed 5-DOF BCCR can achieve stable climbing on curved surfaces.

ABSTRACT This paper presents the Maximizing Circular Margin Discriminant Analysis (MCMDA), a novel feature extraction algorithm designed to address the limitations of traditional methods in handling high‐dimensional data with complex boundaries, local structural complexities, and outlier sensitivity. MCMDA introduces a dual‐strategy framework that integrates a centroid‐based neighbor search and a circular boundary definition to enhance discriminative feature representation. The centroid‐based approach optimizes local neighborhood selection by incorporating both Euclidean distances and geometric distribution analysis, mitigating outlier impacts and improving the accuracy of local structure modeling. The circular boundary strategy defines adaptive decision regions to maximize inter‐class separability while minimizing intra‐class compactness, enabling the extraction of robust features that effectively distinguish complex class boundaries. Through theoretical analysis and experimental validations on benchmark face datasets (ORL, Yale, and AR), MCMDA demonstrates superior performance in classification and recognition tasks compared to state‐of‐the‐art methods. The algorithm achieves higher recognition accuracy, particularly in scenarios with limited training samples or intricate data distributions, by leveraging local geometric structures and boundary information. This research contributes a rigorous methodological framework for feature extraction, addressing the challenges of traditional algorithms in complex data environments and providing robust support for intelligent data analysis and decision‐making processes.

Cleaning high-altitude building windows is traditionally a high-risk task for human workers due to the danger of falls. To enhance safety in facade cleaning and similar tasks, the development of window-cleaning robots has become increasingly essential. However, current robots face challenges in navigating exoskeleton structures due to obstructive beams, limiting their application. This article proposes a novel biped window-cleaning robotic system (BWCRS) designed for exoskeleton structures. The system comprises two key components: a multifunctional biped climbing robot (BCR) and a specialized cleaning robot. The BCR is adept at overcoming obstacles inherent in exoskeleton structures and transitioning across various inclined window surfaces. The BCR adopts a symmetrical construction, and the ends of the feet are equipped with vacuum suction cups and electromagnets, which can switch between different actuators at the geometric base and end of the system. The system is further enhanced by air pressure sensors that monitor the suction cup adhesion in real time, ensuring stable operation. A crucial functionality of the BCR is its manipulation mode, guided by a visual camera. This mode enables precise location and manipulation of the cleaning robot, ensuring effective coverage of all window areas. The mechanical design and mathematical modeling of the BWCRS are thoroughly examined, demonstrating its robust window-cleaning capabilities. The experimental results reveal that the proposed BCR has a favorable window-climbing and manipulation ability and that the BWCRS has a favorable cleaning effect on the exoskeleton-structure windows. Note to Practitioners—This paper is motivated by the problem of window-cleaning for exoskeleton structures, but it also applies to solving window maintenance and inspection. Previous window-cleaning robots were mainly aimed at the facades or surfaces with low obstacles that cannot be applied directly to exoskeleton-structured windows. This paper proposes an exoskeleton-structure window-cleaning method based on a biped window-climbing robot and a cleaning robot. The biped robot can climb over obstacles on the exoskeleton structures and manipulate the cleaning robot to different cleaning areas. Here, we introduce the biped window-climbing robot from the perspective of mechanical design and mathematical kinematics. We then demonstrate multiple obstacle-crossing motion modes of the biped window-climbing robot. Combined with visual-based manipulation, the window-cleaning process for the exoskeleton structures by a biped window-climbing robot integrated with a cleaning robot is demonstrated. The results verify that the proposed design can complete window-cleaning tasks on exoskeleton structures.

Oriented object detection with remote sensing scenes has made excellent progress in recent years, especially using anchor-free detectors. Without the limitation of inherent prior spatial information, anchor-free detectors regress the detection boxes from the object center or edge in an elegant way. However, anchor-free detectors suffer severe feature misalignment and inconsistency between classification and regression. Especially in remote sensing scenes, there are densely arranged instances and multi-scale representations, which will affect the detection accuracy. Therefore, a feature augmentation module (FAM) and an oriented feature alignment (OFA) module are proposed for oriented object detection called FAA-Det. More specifically, we first introduce a FAM to enhance the object representation. After that, the augmented feature maps will be fed into OFA for feature alignment and accurate detection. OFA has two independent branches for classification and regression, and their separate structures can alleviate the inconsistency in detection. FAM and OFA comprise the FAA-Head in our detector. Extensive evaluation demonstrates the effectiveness of our proposed FAA-Det that performs the state-of-the-art (SOTA) mean average precision (mAP) on the DOTA and HRSC2016 datasets without bells and whistles. Our code will be available athttps://github.com/jimuIee/FAA-Det.

Data quality issues exist in practical phasor measurement units (PMUs) due to communication errors or signal interferences. As a result, the performances of existing data-driven disturbance classification methods can be significantly affected. In this article, a fast disturbance classification method that is robust to PMU data quality issues is proposed. The impacts of bad PMU measurements on disturbance classification are investigated by analyzing the feature distributions of deep learning methods. A new feature extraction scheme that uses the univariate temporal convolutional denoising autoencoder (UTCN-DAE) is proposed. It allows encoding and decoding univariate disturbance data through a temporal convolutional network to capture the temporal feature representation and is robust to bad data. Based on the features of the frequency and voltage measurements encoded by the UTCN-DAE, a two-stream enhanced network, i.e., the multivariable temporal convolutional denoising network is proposed to achieve optimal feature extraction of multivariate time series by feature fusion. The classification is performed using a multilayered deep neural network and Softmax classifier. Extensive results obtained on the IEEE 39-bus system as well as a large-scale power system in China with field PMU measurements show that the proposed method achieves the highest classification accuracy and computational efficiency as compared to other deep learning algorithms.

With the development of smart sensing and the aging trend of the society, telerobot is playing an increasingly important role in the field of homecare service. In this article, we present the design, implementation, and evaluation of a wearable sensor glove. The sensor glove is based on inertial measurement units (IMUs) with 9 axes. We put 6 nodes on the glove and calculate the quaternions of all the orientations of the knuckles with fusion algorithm and interpolation algorithm. The main goal of this research is to develop a human-robot interface in order to help to control the telerobot remotely. This paper mainly deals with three issues: 1) designing a simple and extensible motion capture system architecture, 2) choosing a simple but effective fusion algorithm for motion capture, 3) performance evaluation of the sensor glove based on IMUs. The experimental results suggest that the sensor glove we designed can meet the requirements of speed and accuracy. Furthermore, the architecture we designed can not only be used on the sensor glove, it can also be extended to up to 128 nodes to capture the motion of the hole body.