Automatic identification of weld seam types by welding robot is a key link in intelligent welding as some adjustment scheme (e.g., welding trajectory planning, initial welding position, welding parameters) vary with the weld seam types. However, the variable welding environment and various weld seam profile omnifarious affect the robustness of weld seam types identification. To overcome the challenges derived from the weld seam diversity, in this paper, the silhouette-mapping was selected as the weld seam intermedium and a multi-type weld seam automatic identification system based on vision sensor was introduced. Two different laser sources were adopted to obtain robust silhouette-mapping features in proper gestures. Based on the silhouette-mapping data (stripe-mapping and spot-mapping), the related image processing algorithms were carried out to achieve automatic identification. Specifically, the bidirectional deviation search method was proposed to locate the spot-mapping area based on the stripe-mapping image. Aiming at the characteristics of the spot-mapping image, a carefully designed CNN (convolutional neural network) model was used to classify types. Experimental results prove that the silhouette-mapping and CNN are an effective combination for the multi-type weld seam identification, and a total of 97.6% of weld seam types were correctly predicted. Some weld-related studies include welding features extraction, and welding quality detection may improve its accuracy on the basis of determining weld seam types.

In this paper, a complete morphing system consisting of a variable geometry truss manipulator (VGTM) is presented that is fully covered by a flexible panel skin. Two approaches are studied for the morphing control. The first one is to have the VGTM act as a driving mechanism and the flexible panels as a passive system. In this case, the VGTM is composed of active members and passive lockable members. It is shown that the morphing system can reach the desired shapes through intermediate steps. The second method is to have the flexible panels act as drivers and the VGTM as a passive supporting structure. In this case, the VGTM is only composed of passive lockable members. The morphing system can also achieve the desired poses through several steps. The control strategies of the two methods are discussed along with kinematic analysis, a comparison study is conducted to show their pros and cons, two prototypes are fabricated, and experiments are carried out to verify the feasibility of two actuation methods.

Iterative closest point (ICP) is a method commonly used to perform scan-matching and registration. To be a simple and robust algorithm, it is still computationally expensive, and it has been regarded as having a crucial challenge especially in a real-time application as used for the simultaneous localization and mapping (SLAM) problem. For these reasons, this paper presents a new method for the acceleration of ICP with an assisted intensity. Unlike the conventional ICP, this method is proposed to reduce the computational cost and avoid divergences. An initial transformation guess is computed with an assisted intensity for their relative rigid-body transformation. Moreover, a target function is proposed to determine the best initial transformation guess based on the statistic of their spatial distances and intensity residuals. Additionally, this method is also proposed to reduce the iteration number. The Anderson acceleration is utilized for increasing the iteration speed which has better ability than the Picard iteration procedure. The proposed algorithm is operated in real time with a single core central processing unit (CPU) thread. Hence, it is suitable for the robot which has limited computation resources. To validate the novelty, this proposed method is evaluated on the SEMANTIC3D.NET benchmark dataset. According to comparative results, the proposed method is declared as having better accuracy and robustness than the conventional ICP methods.

Crawling robots have elicited much attention in recent years due to their stable and efficient locomotion. In this work, several crawling robots are developed using two types of soft pneumatic actuators (SPAs), namely, an axial elongation SPA and a dual bending SPA. By constraining the deformation of the elastomeric chamber, the SPAs realize their prescribed motions, and the deformations subjected to pressures are characterized with numerical models. Experiments are performed for verification, and the results show good agreement. The SPAs are fabricated by casting and developed into crawling robots with 3D-printing connectors. Control schemes are presented, and crawling tests are performed. The speeds predicted by the numerical models agree well with the speeds in the experiments.

This paper introduces an Adaptive Dynamic Bounded Sliding Mode Control (ADBSMC) method that incorporates a disturbance observer to enhance the response characteristics of the robot manipulator while eliminating the reliance on a priori knowledge. The proposed method utilizes nonlinear sliding mode manifolds and fast-terminal-type convergence laws to address errors and parameter uncertainties inherent in the nonlinear system models. The adaptive law is designed to cover all boundary conditions based on the model’s state. It can dynamically determine upper and lower bounds without requiring prior knowledge. Consequently, the ADBSMC control method amalgamates the benefits of adaptive law and fast terminal sliding mode, leading to significant enhancements in control performance compared with traditional sliding mode control (SMC), exhibiting robustness against uncertain disturbances. To mitigate external disturbances, a system-adapted disturbance observer is devised, facilitating real-time monitoring and compensation for system disturbances. The stability of ADBSMC is demonstrated through the Lyapunov method. Simulation and experimental results validate the effectiveness and superiority of the ADBSMC control scheme, showcasing its potential for practical applications.