Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

• A compact device that is able to simultaneously measure full-field three dimensional deformation of an object has been developed. • A real-time computing scheme enabling quantitative characterization of 3D deformation is proposed. • The 3D deformation of the infrared sensor due to variations of temperature is characterized. • The precision and robustness of the developed techniques have been validated via experiments and simulations. Temperature-induced deformation in the infrared detector chip is a key index for evaluating image aberration and stress concentration. Development of an evaluation means for simultaneous characterization of 3D deformation is of great interest to engineering societies. In this study, an integrated device based on speckle interferometry has been developed for evaluation of the three-dimensional (3D) deformation of the infrared detector chip with variations of temperature. With the use of a 3CCD color camera and a group of optical fibers, the 3D displacement components of the detector surface are measured simultaneously during the cooling process. A temporal phase shifting scheme based on GPU parallel computing has been proposed to enable quantitatively real-time 3D measurement in three image layers at a rate of 20 fps. The integrated device has been validated with a benchmark test. The experimental results show a good agreement with the theoretical solution validating the stability and accuracy of the experimental device. The developed speckle interferometry device provides an accurate means in simultaneously evaluating the 3D deformation of the infrared detector chip due to temperature variations. 

Simultaneous 3D measurement for infrared chips with speckle interferometry

Abstract Skin-like soft sensors are key components for human–machine interfaces; however, the simultaneous sensing of several types of stimuli remains challenging because large-scale sensor integration is required with numerous wire connections. We propose an optical high-resolution multimodal sensing approach, which does not require integrating multiple sensors. This approach is based on the combination of an optical scattering phenomenon, which can encode the information of various stimuli as a speckle pattern, and a decoding technique using deep learning. We demonstrate the simultaneous sensing of three different physical quantities—contact force, contact location, and temperature—with a single soft material. Another unique capability of the proposed approach is spatially continuous sensing with an ultrahigh resolution of few tens of micrometers, in contrast to previous multimodal sensing approaches. Furthermore, a haptic soft device is presented for a human–machine interface. Our approach encourages the development of high-performance smart skin-like sensors. 

/pdf/speckle-based-high-resolution-multimodal-soft-sensing-377c7yyw.pdf

Speckle-based high-resolution multimodal soft sensing

Eemd-Based Videogrammetry and Vibration Analysis Method for Rotating Wind Power Blades

EEMD-based videogrammetry and vibration analysis method for rotating wind power blades

Monitoring the blades of a wind turbine by using videogrammetry

With the development of automobile industry, the license plate, as the unique identifier of the vehicle, plays an important role in vehicle management. License plate recognition technology is mainly realized by image processing technology. It mainly includes image preprocessing, license plate positioning, character segmentation and other processes. In the actual process of license plate recognition, it is often impossible to accurately recognize the license plate due to the positional relationship such as angle. Therefore, this paper proposes a license plate recognition program based on the capsule network. First, through image preprocessing, license plate positioning, and character segmentation, and then use capsule neural network for training and simulation in order to achieve the object of accurate character recognition.

Design of license plate recognition system based on capsule network

Accurate and fast speckle interferometry in severe illumination conditions

In order to realize the path tracking of autonomous vehicles, this paper first simplifies the complex structure of the vehicle into a two-degree-of-freedom kinematic and dynamic vehicle model. based on PID control, the vehicle path tracking controller is designed to calculate the lateral deviation between the expected path and the actual path, and outputs the front wheel rotation angle through the controller, so that the autonomous vehicle can travel well according to the expected path. The control system model is built in the MATLAB/Simulink, and the vehicle path tracking control system is simulated and verified jointly with the CarSim software platform. Simulation results show that the designed control strategy can ensure the path tracking performance of intelligent vehicles at different speeds, and has good tracking accuracy, real-time performance and vehicle driving stability.

https://robots.iopscience.iop.org/article/10.1088/1757-899X/892/1/012050/pdf

Dongsheng Zhang

Papers

Monitoring the blades of a wind turbine by using videogrammetry

Design of license plate recognition system based on capsule network

Accurate and fast speckle interferometry in severe illumination conditions

Driverless simulation of path tracking based on PID control

Research on pavement garbage detection based on machine vision