In recent decades, haze has become an environmental issue due to its effects on human health. It also reduces visibility and degrades the performance of computer vision algorithms in autonomous driving applications, which may jeopardize car driving safety. Therefore, it is extremely important to instantly remove the haze effect on an image. The purpose of this study is to leverage useful modules to achieve a lightweight and real-time image-dehazing model. Based on the U-Net architecture, this study integrates four modules, including an image pre-processing block, inception-like blocks, spatial pyramid pooling blocks, and attention gates. The original attention gate was revised to fit the field of image dehazing and consider different color spaces to retain the advantages of each color space. Furthermore, using an ablation study and a quantitative evaluation, the advantages of using these modules were illustrated. Through existing indoor and outdoor test datasets, the proposed method shows outstanding dehazing quality and an efficient execution time compared to other state-of-the-art methods. This study demonstrates that the proposed model can improve dehazing quality, keep the model lightweight, and obtain pleasing dehazing results. A comparison to existing methods using the RESIDE SOTS dataset revealed that the proposed model improves the SSIM and PSNR metrics by at least 5–10%.
In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.
Filament winding reinforcement is often applied to fulfill high-pressure resistance and is lightweight for gas cylinder productions. This article analyzes the winding pattern and the corresponding characteristics for filament winding cylinders based on the resulting thickness and strength such that the gas cylinders can be made as light as possible. In order to prevent the sliding between the filament material and the cylinder surface during the winding process, a range of winding angles that do not exceed the maximum static friction at every instant is adopted. The gas cylinder geometric structure formed by a complete round of winding using different winding angles is calculated to find the winding pattern that consumes the least composite filament. The winding pattern can also be determined before production to reduce the cost and time for customized products. By sequential contact points of the winding process, motion planning can be carried out for a four-axis filament winding machine.
In this study, the performance of a vortex array gripper was numerically investigated based on the pressure distribution on the surface of a gripped object and the resulting suction force. An analysis of the suction force generated by a single-vortex gripper was performed to determine the geometric parameters for providing a good suction force and subsequently, for the vortex array gripper configuration. Array grippers consisting of two- and four-vortex grippers were studied. For dual-vortex grippers, the generated suction forces of various inlet air configurations with different vortex gripper distances are illustrated. The pros and cons of all types of air supply and the influence of positive pressure formed by outlet airflow interaction were examined. The analysis of quad-vortex grippers also revealed that the suction force could be increased by reducing the outlet flow interaction between the grippers using the placement of exhaust vents. Thus, the installation of array grippers can be arranged in a more compact form to increase the total suction force per unit operation area with uniformity.
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