Recently, laser vision sensors are widely applied in initial point alignment and seam tracking to improve the level of intelligent welding because of good characteristics. However, since the deformation of laser stripe is unobvious at the narrow weld with 0.2mm width, these methods are not applicable for the narrow weld. Moreover, there are rare researches could achieve initial point alignment and seam tracking of narrow weld simultaneously. Therefore, an initial point alignment and seam tracking system for narrow weld is proposed in this paper. At first, a laser vision sensor with extra LED light is used to obtain laser and weld seam image. Besides, the seam feature point is extracted and three dimensional (3D) coordinates could be obtained with vision model. In addition, three controllers including decision controller, initial point alignment controller and seam tracking controller are proposed to achieve initial point alignment and seam tracking control in X and Z axis directions. What's more, feature verification, Kalman Filter (KF) and output pulse verification are designed to improve the accuracy and stability of this system. Finally, many initial point alignment and seam tracking experiments of narrow weld are conducted. Experiment results demonstrate that proposed system could well achieve initial point alignment and seam tracking of planar and curved surface narrow weld.

Underwater structured light vision system has the characteristics of large amount of information, high measurement accuracy and good robustness, which has attracted the attention of researchers and widely used in underwater three dimensional (3D) measurement. In this paper, the state of the art techniques of underwater structured light vision system is comprehensively summarized, which provides valuable guidance for relevant researchers. Firstly, underwater structured light vision systems are classified and introduced, and their advantages and disadvantages are analyzed. Then the calibration methods of underwater structured light vision system are introduced, including underwater camera calibration, underwater laser calibration and underwater projector calibration. Besides, the image processing methods of underwater structured light vision system are analyzed, including laser structured light image processing and coded structured light image processing. Moreover, the applications of underwater structured light vision system in underwater archaeology, seabed mapping and underwater structure inspection are introduced. On this basis, the research directions of underwater structured light vision system are prospected.

The three dimensional (3D) perception of the low-light underwater environment has always been a major challenge which greatly limits the underwater operations. In this paper, an underwater active vision measurement system based on binocular structured light is designed to achieve high precision 3D reconstruction. Firstly, the fusion technology of binocular camera and laser addresses underwater optical attenuation and feature sparsity. Then, in order to avoid the huge inertia caused by the overall movement of the system, a laser scanner based on the mirror-galvanometer is used to accomplish static scanning of the scene. Subsequently, considering the influence of multiple media, underwater refraction models including monocular imaging model, binocular ranging model, and binocular polar curve constraint model are systematically proposed. What’s more, the conventional checkerboard-based passive visual calibration method is ineffective for low-light waters. Therefore, a simple calibration block is designed, and a new multi-objective laser-based calibration algorithm based on laser geometric constraints is proposed. Finally, the effectiveness of the system is verified by analyzing the 3D reconstruction results of underwater objects.

Seam feature point acquisition is the premise of the intelligent welding process such as initial point guiding and seam tracking. However, conventional seam feature point acquisition methods based on geometric feature have shortcomings of poor flexibility and robustness. In this paper, a seam feature point acquisition method based on efficient convolution operator (ECO) and particle filter (PF) is proposed, which could be applied to different weld types and could achieve fast and accurate seam feature point acquisition even under the interference of welding arc light and spatter noises. Firstly, a structured light vision sensor is developed to acquire welding image. Secondly, ECO algorithm is adopted to track the seam region and acquire seam feature point during gas metal arc welding (GMAW) process. Thirdly, the state and measurement equations of the weld seam position are established, and PF is applied to improve seam feature point acquisition accuracy. Finally, a welding experiment system is built and a series of seam feature point acquisition experiments of butt joint, lap joint and fillet joint are carried out to validate the performance of the proposed method. The experiment results demonstrate that the processing speed of the proposed method could reach up 35Hz, and the seam feature point acquisition errors are smaller than 0.15mm, which could meet the real-time and accuracy requirement for subsequent initial point guiding and seam tracking.

Due to the complex mechanism and fabrication process of flexible materials, it remains extremely challenging for a flexible robotic fish to achieve fast and efficient locomotion. In this article, taking advantage of the passive bending and energy storage properties of flexible materials, we propose an untethered robotic fish with multiple flexible joints to achieve high performance and low Cost of Transport (COT). First, combining rigid links and flexible materials, a compact flexible tail with a simple and efficient structure is proposed. Next, the pseudo-rigid body theory is applied to analyze the deformation of passive joints, and a full-state dynamic model is established. More importantly, an optimization method by adjusting the phase differences of the passive joints is used to obtain high aquatic performance. Finally, extensive simulations and experiments validate the effectiveness of the proposed method, and the robotic fish can achieve a maximum speed of 1.63 body length (BL) per second and a minimum COT of 4.8 J/m (2.87 J/m $\cdot$ kg). Compared with the multi-joint robotic fish with a similar design, the COT is reduced by up to 81.05% with the basically same aquatic ability. Excitingly, the flexible robotic fish can achieve a COT of 7.36 J/m at 1.23 BL/s, which is 15.72%-36.34% lower than that of the bluefin tuna and is within the range of yellowfin tuna, offering valuable insight into high speed and long endurance applications for underwater robots. Note to Practitioners –This paper is motivated by the design and optimization of an efficient bionic flexible underwater robot with high aquatic abilities, which is conducive to aquatic tasks that require long-time and long-distance sailing, such as underwater topographic exploration, submarine archaeology, and underwater search and rescue. Existing studies of free-swimming bionic underwater robots usually focus on the improvement of swimming speed and rarely consider achieving both high swimming performance and low energy cost. Thus, this paper proposes a bionic underwater robot design with two joints made of flexible materials on the tail to address the problem. Based on detailed analyses of the hydrodynamic force and flexible joint deformation, we propose an effective optimization method for swimming performance. A series of simulations and experiments suggest that the mechatronic design and optimization method are practical and valid. Hopefully, our design and method can provide theoretical guidance for engineers to design and optimize robots with flexible joints.

The inspection of underwater pipeline by underwater vehicles is of great significance to ensure safe transportation. However, most of underwater pipeline inspection methods have disadvantages such as low precision, low resolution and less information, and cannot realize the fine three-dimensional (3D) reconstruction of underwater pipelines. In order to address these problems, an underwater pipeline tracking and 3D reconstruction method for underwater vehicle based on structured light vision (SLV) is proposed. Firstly, a dual-line laser SLV is developed, and a new underwater pipeline positioning method is proposed, which can simultaneously obtain the lateral deviation, height deviation and heading deviation of underwater vehicle and underwater pipeline under weak light water environment. Then, by combining laser stripe image feature points, refracted underwater SLV model and Doppler Velocity Log (DVL) information, underwater pipeline tracking and dense 3D reconstruction are realized. Finally, by integrating the self-designed underwater SLV sensor with the underwater vehicle BlueROV, an underwater pipeline tracking and 3D reconstruction system is developed. A series of planar and spatial pipeline tracking and 3D reconstruction experiments demonstrate the effectiveness of the proposed method.