Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The proposed method extracts lines from images using a line filter and a Hough transform, and it uses a convolutional neural network to robustly extract parking lines from the environment. In addition, a parking path consisting of curved and straight sections is created and used to control the vehicle. Perpendicular, angle, and parallel parking paths can be created; however, parking control is applied according to the shape of each parking slot. The results of our experiments confirm that the proposed method has an average offset of 10.3 cm and an average heading angle error of 0.94°.

An Efficient Vehicle Localization Method by Using Monocular Vision

Accurate localization of surrounding vehicles helps drivers to perceive surrounding environment, which can be obtained by two parameters: depth and direction angle. This research aims to present a new efficient monocular vision based pipeline to get the vehicle’s location. We proposed a plug-and-play convolutional block combination with a basic target detection algorithm to improve the accuracy of vehicle’s bounding boxes. Then they were transformed to actual depth and angle through a conversion method which was deduced by monocular imaging geometry and camera parameters. Experimental results on KITTI dataset showed the high accuracy and efficiency of the proposed method. The mAP increased by about 2% with an additional inference time of less than 5 ms. The average depth error was about 4% for near distance objects and about 7% for far distance objects. The average angle error was about two degrees.

Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

Background and Objective: Ultrasound (US) devices are often used in percutanous interventions. Due to their low image quality, the US image slices are aligned with pre-operative Computed Tomography/Magnetic Resonance Imaging (CT/MRI) images to enable better visibilities of anatomies during the intervention. This work aims at improving the deep learning one shot registration by using less loops through deep learning networks.Methods: We propose two cascade networks which aim at improving registration accuracy by less loops. The InitNet-Regression-LoopNet (IRL) network applies the plane regression method to detect the orientation of the predicted plane derived from the previous loop, then corrects input CT/MRI volume orientation and improves the prediction iteratively. The InitNet-LoopNet-MultiChannel (ILM) comprises two cascade networks, where an InitNet is trained with low resolution images toperform coarse registration. Then, a LoopNet wraps the high resolution images and result of the previous loop into a three channel input and trained to improve prediction accuracy in every loop. Results: We benchmark the two cascade networks on 1035 clinical images from 52 patients , yielding an improved registration accuracy with LoopNet. The IRL achieved an average angle error of 13.3° and an average distance error of 4.5 millimieter. It out-performs the ILM network with angle error 17.4° and distance error 4.9 millimeter and the InitNet with angle error 18.6° and distance error 4.9 millimeter. Our results show the efficiency of the proposed registration networks, which have the potential to improve the robustness and accuracy of intraoperative patient registration.

Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller

With the development of robot technology, integrated joints with small volume and convenient installation have been widely used. Based on the double inertia system, an integrated joint motor servo system model considering gear angle error and friction interference is established, and a joint control strategy based on BP neural network and pole assignment method is designed to suppress the vibration of the system. Firstly, the dynamic equation of a planetary gear system is derived based on the Lagrange method, and the gear vibration of angular displacement is calculated. Secondly, the vibration displacement of the sun gear is introduced into the motor servo system in the form of the gear angle error, and the double inertia system model including angle error and friction torque is established. Then, the PI controller parameters are determined by pole assignment method, and the PI parameters are adjusted in real time based on the BP neural network, which effectively suppresses the vibration of the system. Finally, the effects of friction torque, pole damping coefficient and control strategy on the system response and the effectiveness of vibration suppression are analyzed.

Optimal Guidance Law with Impact-Angle Constraint and Acceleration Limit for Exo-Atmospheric Interception

This paper aims to develop an optimal guidance law for exo-atmospheric interception, in which impact-angle constraint and acceleration limit are considered. Firstly, an optimal control problem with constraints on terminal miss and impact-angle is formulated, in which the control energy performance index is weighted by a power function of the time-to-go. The closed-loop guidance command, which is expressed as a linear combination of zero-effort miss distance and the zero-effort angle error, is derived using a traditional order reduction transformation. Then, an analytical solution to the maximal acceleration during the flight is obtained by analyzing the boundary points and critical points of the guidance command curve. It is found that the maximal acceleration is a function of the weighted gain in the performance index. Therefore, the maximal acceleration can be efficiently limited by using the variable weighted gain. Furthermore, the relationship between the total control energy and the weighted gain is studied. As a result, a systematic method is proposed for selecting the weighted gain so as to meet the constraint of the acceleration while the total control energy is minimal. Nonlinear simulations have been carried out to test the performance of the proposed method. The results show that this method performs well in intercepting the maneuvering target with a negligible miss distance and intercept angle error. And it can tolerate a stricter acceleration limit in comparison with the typical method.

A Novel Distal Interlocking Screw Guidance System Using a Laser Pointer and a Mechanical Fine-Adjustment Device

In minimally invasive bone fracture reduction surgery, broken femur bones are firmly fixed to a metallic intramedullary nail (IMN) after they are properly aligned. One of the greatest challenges of this process is that surgeons cannot directly see holes on the IMN, which increases the difficulty of the procedure and results in the requirement of taking a large number of X-ray images to find the location and direction of holes. We propose a novel distal interlocking screw guidance system that consists of a parallel guidance system using a laser pointer (PGSLP) and a mechanical fine-adjustment device (FAD). The PGSLP is used to make the planes of the C-arm and FAD parallel. The FAD is used to concentrically align the IMN hole with the guiding hole. The performance of the proposed device was evaluated by a series of experiments. The tilted angle error between the C-arm and FAD was measured to be 1.24 ± 0.715°. The translational error between the IMN hole and guiding hole was measured to be 0.378 ± 0.120 mm. Since the proposed guiding system is simple, cost-effective, and accurate, we expect it will soon be used in real operations.

The Research on Anti-DC current Transformer of Dual Iron Core Structure with Air Gap

Current transformer (CT) is wildly used in electrical measurement and relay protection. In order to improve the power system stability in DC bias, the CT performance of anti-DC is necessarily to be enhanced. Based on the Jiles-Atherton theory, the magnetization characteristics of iron core with different air gap was analyzed in this paper. A simulation model was established using the Simulink toolbox, and the ratio error and angle error were investigated in different air gap length. Simulation result shows that the maximum magnetic density of iron core almost stays uncharged with the addition of air gap. Furthermore the slope of magnetization curve decreased, which leads to the increase of iron core saturation current. Current transformer with closed and air gap iron core possesses a stable measurement error in condition of severe DC bias.

Against the leans: Overcoming spatial disorientation through galvanic vestibular stimulation

Leans illusion is a type of Spatial Disorientation (SD) that pilots often experience which can adversely affect flight performance. For pilots’ flight safety, research on how to effectively overcome SD such as leans illusion is important. The purpose of this study is to identify the overcoming effect of Galvanic Vestibular Stimulation (GVS) technology on leans illusion. Twenty-one Air Force pilots participated in a flight simulation experiment where leans illusion was induced through a specialized SD simulator. In the with-GVS condition, GVS was given during the roll-out phase. Data was analyzed using roll angle error and subjective SD scales by two conditions (with-GVS, without-GVS). Results showed that both the roll angle error and the subjective SD scale scores were found to be lower in the with-GVS condition than in the without-GVS condition. This study suggests that the use of GVS technology can potentially contribute in overcoming leans illusion.