This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions P(x,y ) and W(x,y ) and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.

A suitable trajectory in a port inspection mission is important for unmanned aerial vehicles (UAVs). Motion planning can help UAVs quickly generate an optimal trajectory that meets the constraints. The motion planning of UAVs is achieved in this paper as follows: firstly, a collision detection (CD) function is applied that evaluates whether the bias_RRT* (rapidly exploring random tree) algorithm needs to be called. Secondly, an isosceles triangle optimization function optimizes the path. Next, a trajectory is generated based on the minimum snap trajectory method. Lastly, the bias_RRT* algorithm and the improved bias_RRT* algorithm are used in the two experimental scenes for path planning comparison, and trajectory planning is carried out. The results show that, in the improved method, the path length and calculation time are shortened, and the trajectory cost and trajectory deviation are also significantly reduced. Overall, it appears that a camera-equipped UAV under the proposed approach can accomplish monitoring tasks more effectively and safety in port environment.

Crystallization is important to the pharmaceutical, the chemical, and the materials fields, where the morphology of crystals is one of the key factors affecting the quality of crystallization. High-throughput screening based on microfluidic droplets is a potent technique to accelerate the discovery and development of new crystal morphologies with active pharmaceutical ingredients. However, massive crystal morphologies’ datum needs to be identified completely and accurately, which is time-consuming and labor-intensive. Therefore, effective morphologies’ detection and small-target tracking are essential for high-efficiency experiments. In this paper, a new improved algorithm YOLOv8 (YOLO-PBESW) for detecting indomethacin crystals with different morphologies is proposed. We enhanced its capability in detecting small targets through the integration of a high-resolution feature layer P2, and the adoption of a BiFPN structure. Additionally, in this paper, adding the EMA mechanism before the P2 detection head was implemented to improve network attention towards global features. Furthermore, we utilized SimSPPF to replace SPPF to mitigate computational costs and reduce inference time. Lastly, the CIoU loss function was substituted with WIoUv3 to improve detection performance. The experimental findings indicate that the enhanced YOLOv8 model attained advancements, achieving AP metrics of 93.3%, 77.6%, 80.2%, and 99.5% for crystal wire, crystal rod, crystal sheet, and jelly-like phases, respectively. The model also achieved a precision of 85.2%, a recall of 83.8%, and an F1 score of 84.5%, with a mAP of 87.6%. In terms of computational efficiency, the model’s dimensions and operational efficiency are reported as 5.46 MB, and it took 12.89 ms to process each image with a speed of 77.52 FPS. Compared with state-of-the-art lightweight small object detection models such as the FFCA-YOLO series, our proposed YOLO-PBESW model achieved improvements in detecting indomethacin crystal morphologies, particularly for crystal sheets and crystal rods. The model demonstrated AP values that exceeded L-FFCA-YOLO by 7.4% for crystal sheets and 3.9% for crystal rods, while also delivering a superior F1-score. Furthermore, YOLO-PBESW maintained a lower computational complexity, with parameters of only 11.8 GFLOPs and 2.65 M, and achieved a higher FPS. These outcomes collectively demonstrate that our method achieved a balance between precision and computational speed.

Measuring geometric properties for large-sized parts is faced with various challenges. This study established a novel method for measuring the flatness of large-scale annular workpieces, with the flange of a wind turbine tower as an example. The proposed method is based on three-dimensional (3D) machine vision and precision motion control technology, aiming to address issues such as distorted measurements, long measurement times, and significant safety risks. The study primarily focused on the development of algorithms for measuring the flatness of large-sized annular workpieces through region-based measurement and vertical centering. The system employs a rotating arm equipped with a 3D vision sensor driven by a precision motion control system. A constant-speed laser line scans the surface of the large-sized flange to acquire precise point cloud data. The obtained point cloud data are then processed to determine the flatness of the flange surface. A verification test was conducted on a wind turbine tower flange with a diameter of 4.2 m to validate the functionality of the system. The results indicated that the proposed system enables stable and accurate measurement of flange flatness, and the measurement uncertainty was analyzed using the guide to the uncertainty in the measurement method, yielding an expanded uncertainty of 0.072 mm. The results indicated that the developed flatness measurement system can efficiently, accurately, and consistently measure the flatness of large annular flanges. This provides valuable insights for the geometric measurement of large annular parts in various fields such as wind power, engineering machinery, ships, and military industries.

Vanadium oxide-based electrode materials have attracted increasing attention owing to their extraordinary capacitance and prolonged lifespan, excellent conductivity and outstanding electrochemical reversibility. However, the development of vanadium oxide-based integrated electrodes with outstanding capacitive performance is an enduring challenge. This research reports a facile method for structuring 3D Archimedean micro-supercapacitors (AMSCs) composed of Mo doped V–VO_0.2 (Mo@V–VO_0.2) based integrated electrodes with designable geometric shape, using computer-aided wire electric discharge machining (WEDM). The performance of Mo@V–VO_0.2 based AMSCs manufactured by different processing voltages of 60 V, 80 V and 100 V were evaluated. It was found that 80 V is the optimal processing voltage for manufacturing Mo@V–VO_0.2 based AMSCs with the best electrochemical performance. This device demonstrates superior capacitive behavior even at an ultra-high scan rate of 50, 000 mV s⁻¹, and achieves a good capacitance retention rate of 94.4% after 2000 cycles. Additionally, the characteristics of electric field distribution were also simulated for optimizing the geometric structure of the microdevices. This WEDM fabrication technique, which is easy, secure, patternable, efficient, economical, eco-friendly, and does not require binders or conductive additives, enables the development of high-capacity 3D pseudocapacitive micro-supercapacitors and demonstrates the great potential for metal oxide synthesis and microdevice manufacturing.

With the rapid development of marine engineering in recent years, offshore operations have become increasingly common, making wave compensation platforms crucial for safe operations at sea. This paper presents a pendulum-type wave compensation platform specifically designed for wave compensation applications. The main components of this wave compensation platform include a chassis, support base, hydraulic cylinders, telescopic rods, upper platform, three sets of balancing mechanisms, three sets of tilt angle sensors, and a control system. Firstly, to thoroughly understand the compensatory motion of the pendulum-type three-degree-of-freedom wave compensation platform, kinematic analysis of the entire system was conducted, and the motion inverse solution curves of the mechanism were obtained through simulation using motion simulation software. Secondly, to enhance the compensatory response performance of the platform, a fuzzy PID control algorithm was employed to control the system and achieve attitude control of the platform. Finally, through control system simulation, compared to PID control, fuzzy PID reduces system response delay and successfully meets the expected technical requirements and application needs.