Electrical discharge machining has emerged as one of the most accepted non-traditional machining methods that have the capability of attaining complex shapes and better feature size in difficult-to...

Micro-electrical discharge machining of difficult-to-machine materials: A review:

The rapid development of artificial intelligence leads to many studies on autonomous robot and self-driving vehicles, in which, autonomous driving plays one of the most important roles in supporting a robot or a car to be able to observe, move, and avoid obstacles. In this paper, a new method is proposed to detect road lane markings for supporting surveillance and autonomous driving. Images captured from a front-view camera are fed forward into a semantic segmentation network to extract features for detecting road lane markings, the network is constructed based on U-Net architecture, a convolutional neural network developed for biomedical image segmentation, then Hough Transform method is implemented in the system to determine lines in the segmentation network outcomes. In addition, Hough Transform yields plenty of lines from segmented images, thus K-means Clustering algorithm is also investigated to compute and point out the fittest line with each road lane marking. The effectiveness of the system was validated by testing on CARLA simulator, an open-source simulator for research on autonomous driving. Experiments proved that the proposed method can work with favorable results.

Robust U-Net-based Road Lane Markings Detection for Autonomous Driving

Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

/pdf/lane-detection-under-artificial-colored-light-in-tunnels-and-1676o8xeug.pdf

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Due to inevitable wear of electrodes in micro electrical discharge machining (μEDM), compensation of tools is of great importance to maintain machining accuracy. Among all of the compensation strategies, fix-length compensation method is one of the most efficient ways for cavity milling because large single layer thickness can be achieved. However, previous studies have shown that slots with triangle-shape cross section are formed, and extra processing has to be performed to remove the surface fluctuations, which degrade machining accuracy and lower total efficiency. To improve the traditional fix-length compensation method, in this work, the cylindrical tool is replaced by the tubular tool, and a truncated conic tool end is observed in experiments. An analytical model is then developed relating the truncated conic shape to fix-length compensation parameters. This analytical model is then used to determine the machining parameters, which correspond to a preset milling depth. The effects of key parameters on plane machining are predicted by the analytical model and verified through double slots machining experiments, which show high machining accuracy can be reached under any overlapping ratios and target depths. Finally, the presented machining method is further validated by machining a rectangular cavity and a cavity with a circular pattern. Machining results prove the depth accuracy of the proposed method can be controlled within 4 μm.

An improved fix-length compensation method for electrical discharge milling using tubular tools

Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras.

A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on Highway

Micro electrical discharge machining (EDM) has been widely applied for manufacturing precise and complex 3D micro shapes. However, in 3D micro EDM machining, electrode wear, caused by discharged pulses, is a serious problem, which significantly affects machining accuracy. In this work, a real-time, micro EDM monitoring system was developed to classify and count the number of discharge pulses using the uniform wear method. Based on the normal number of discharges and tool wear, an empirical relationship between them was established. From this relationship, this study also proposed a new electrode wear compensation method, called as NNU method, based on the number of normal discharges and uniform wear method (UWM). Experimental results indicated that the grooves or 3D shapes could be fabricated with high accuracy.

Precision micro EDM based on real-time monitoring and electrode wear compensation

In this work, we introduce an approach to detect information about lanes and vehicles for driver assistance, or warning system Most previous research works could only detect the lanes or vehicles separately However, the combination of lane information and vehicle information is able to support the lane keeping system, or driver assistance system, and to improve the reliability results For driver assistance system, it must detect multi-lanes (left, right side lane, and middle lane), and discover vehicles ahead of the test vehicle on these lanes Therefore, in this study, we use a camera, it is installed on the windscreen of the test vehicle Images from the camera are used to detect three lanes, and detect multi-vehicles In lane detection, line detection and inverse perspective mapping are used For the vehicle detection, we combine the horizontal and vertical edge detection, the horizontal edge is used to detect the vehicle candidates, and then the vertical edge detection is used to verify the vehicle candidates The proposed algorithm works in an average of 42 milliseconds for each frame with resolution on a 330 GHz Intel CPU The system was tested on the highway in Korea

A study on detection method of vehicle based on lane detection for a driver assistance system using a camera on highway

Red-emitting α-Al2O3:Mn4+ fluorescent powders were prepared by co-precipitation method and sintered at1000 to 1500oC in the air. Effects of sintering temperature and Mn4+ concentration on the optical properties of the materials were investigated. The X-ray diffraction (XRD) showed that the crystallinity of Al2O3 belonged to hexagonal with R3C space group. The excitation spectrum consisted of two broad intense bands centered at 399 and 568 nm and was assigned to 4A2g(F)→4T1g(F) and 4A2g(F)→4T2g(F) transitions, respectively. Under the excitation wavelengths of 399 and 568 nm, the α-Al2O3:0.1% Mn4+ phosphors emitted efficient red light at 694 nm caused by 2Eg→4A2g transition. A red LED is made by coating Al2O3:0.1% Mn4+ powder on a violet chip located in the deep-red light region. These results showed that α-Al2O3:Mn4+has the potential to produce red LED lightings for plants.

Synthesis and luminescence properties of deep-red emitting Mn4+ doped α-Al2O3 for professional LED grow light

In this work, we introduce an approach to detect vehicles for driver assistance, or warning system. For driver assistance system, it must detect both lanes (left and right side lane), and discover vehicles ahead of the test vehicle. Therefore, in this study, we use a camera, it is installed on the windscreen of the test vehicle. Images from the camera are used to detect three lanes, and detect multiple vehicles. In lane detection, line detection and vanishing point estimation are used. For the vehicle detection, we combine the horizontal and vertical edge detection, the horizontal edge is used to detect the vehicle candidates, and then the vertical edge detection is used to verify the vehicle candidates. The proposed algorithm works with of 480 × 640 image frame resolution. The system was tested on the highway in Korea.

Van Quang Nguyen

Papers

A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on Highway

Precision micro EDM based on real-time monitoring and electrode wear compensation

A study on detection method of vehicle based on lane detection for a driver assistance system using a camera on highway

Synthesis and luminescence properties of deep-red emitting Mn4+ doped α-Al2O3 for professional LED grow light

A study on obstacle detection method of the frontal view using a camera on highway