Biped wall-climbing robots well adapt for carrying out tasks in large-scale 3D environments such as skeleton-facade structures, due to their good abilities to transit between walls and surmount obstacles. However, to plan 3D collision-free climbing paths in such environments is challenging and significant for biped wall-climbing robots. In this paper, we propose a novel hierarchical framework for planning 3D biped climbing navigation, including three levels, namely the global route, the foothold, and the single-step motion. We also present three effective planners incorporated in the framework to solve this challenging planning problem efficiently. A global route planner is designed on top, for rapidly analyzing the possibility for the robot to transit between walls, and searching walls that must be passed through and key footholds for performing transition, in order to optimize the route length globally. A local foothold planner is implemented in the middle, to search and optimize footholds on each via wall, considering the robot's ability to step over, step on, and bypass obstacles. A single-step motion planner is deployed at the bottom, for planning collision-free movement swinging the robot suction module from a foothold to another. Simulations are conducted to fully verify the effectiveness and performance of the presented planners. Experiments with W-Climbot are carried out to demonstrate that the proposed framework and planners succeed in planning 3D collision-free optimized climbing paths for biped wall-climbing robots.

Biped climbing robots are considered good assistants and (or) substitutes for human workers carrying out high-rise truss-associated routine tasks. Flexible locomotion on three-dimensional complex trusses is a fundamental skill for these robots. In particular, the capability to transit from one structural member to another is paramount for switching objects to be climbed upon. In this paper, we study member-to-member transition and its utility in global path searching for biped climbing robots. To compute operational regions for transition, hierarchical inspection of safety, reachability, and accessibility of grips is taken into account. A novel global path rapid determination approach is subsequently proposed based on the transition analysis. This scheme is efficient for finding feasible routes with respect to the overall structural environment, which also benefits the subsequent grip and motion planning. Simulations are conducted with Climbot, our self-developed biped climbing robot, to verify the efficiency of the presented method. Results show that our proposed method is able to accurately determine the operational region for transition within tens of milliseconds and can obtain global paths within seconds in general.

Biped climbing robots (BiCRs) can overcome obstacles and perform transition easily thanks to their superior flexibility. However, to move in a complex truss environment, grips from the original point to the destination, as a sequence of anchor points along the route, are indispensable. In this paper, a grip planning method is presented for BiCRs generating optimal collision-free grip sequences, as a continuation of our previous work on global path planning. A mathematic model is firstly built up for computing the operational regions for negotiating obstacle members. Then a grip optimization model is proposed to determine the grips within each operational region for transition or for obstacle negotiation. This model ensures the total number of required climbing steps is minimized and the transition grips are with good manipulability. Lastly, the entire grip sequence satisfying the robot kinematic constraint is generated by a gait interpreter. Simulations are conducted with our self-developed biped climbing robot (Climbot), to verify the effectiveness and efficiency of the proposed methodology.

Biped climbing robots have the character of multi-source asynchronous sensing, and they are powerful to replace humans to complete high-altitude and high-risk operations. As the key to autonomous climbing and visual perception, the VSLAM (Visual Simultaneous Localization And Mapping) for biped climbing robots is studied in this paper. Due to the limited computing power of the biped climbing robot, a cloud-based VSLAM system is introduced to offload the high computation. Considering the limited transmission in the high-altitude climbing environment, we propose a submap-based cloud-edge collaboration mechanism, in which a multiple submap VSLAM system is built. To satisfy the multiple source sensing of the biped climbing robot, we design an asynchronous submap building framework. According to the character of VSLAM, the edge-cloud transmission is optimized to reduce the bandwidth. The evaluations have verified our advanced performance in terms of bandwidth cost and tracking precision. Also, the experiment on the real climbing robot demonstrates our feasibility and superiority. Our Cloud-based Asynchronous Multi-submap VSLAM (CAMs-SLAM) system enables biped climbing robots to environment perception and achieves mapping and localization for later autonomous navigation.

Biped wall-climbing robots (BWCRs) serve as viable alternatives to human workers for inspection and maintenance tasks within three-dimensional (3D) curtain wall environments. However, autonomous climbing in such environments presents significant challenges, particularly related to localization and navigation. This paper presents a pioneering navigation framework tailored for BWCRs to navigate through 3D curtain wall environments. The framework comprises three essential stages: Building Information Model (BIM)-based map extraction, 3D climbing path planning (based on our previous work), and path tracking. An algorithm is developed to extract a detailed 3D map from the BIM, including structural elements such as walls, frames, and ArUco markers. This generated map is input into a proposed path planner to compute a viable climbing motion. For path tracking during actual climbing, an ArUco marker-based global localization method is introduced to estimate the pose of the robot, enabling adjustments to the target foothold by comparing desired and actual poses. The conducted experiments validate the feasibility and efficacy of the proposed navigation framework and associated algorithms, aiming to enhance the autonomous climbing capability of BWCRs.

Detecting weld surface defects under low-light conditions presents significant challenges due to limited illumination and subtle defect features. In this study, we propose a Visual Inspection System for Weld Surface Defects (VISFWSD). To support training and evaluation, we construct a dedicated Weld Surface Defect Data Set (WSDDS). Building upon the YOLOv8n architecture, we introduce DCE-YOLOv8n, a lightweight and high-performance model enhanced with Deformable Convolutional Networks (DCNv3) and the Convolutional Block Attention Module (CBAM) to improve multi-scale and fine-grained defect feature extraction. Furthermore, we adopt the Enhanced Intersection over Union (EIoU) loss function to address training sample imbalance and enhance localization accuracy. The proposed DCE-YOLOv8n model achieves superior performance across multiple evaluation metrics compared to baseline models. Specifically, it attains the highest mAP@0.5 of 92.9% and mAP@0.5:0.95 of 64.5%, outperforming YOLOv8n by 2.7 and 3.1 percentage points, respectively. Despite these accuracy gains, DCE-YOLOv8n maintains competitive computational efficiency, with 7.6 GFLOPS and 2.4 million parameters, both slightly lower than those of YOLOv8n (8.0 GFLOPS and 3.1 million parameters). Furthermore, it achieves an inference speed of 200 Frames Per Second (FPS), satisfying real-time processing requirements. These results demonstrate that DCE-YOLOv8n effectively balances detection accuracy, model size, and computational cost, making it well-suited for practical deployment in low-light weld defect inspection tasks. The proposed VISFWSD and DCE-YOLOv8n together provide an effective and efficient solution for weld surface defect detection in low-light environments.