Design of Intelligent Vehicle Rear Vision System

2017 ◽  
Vol 863 ◽  
pp. 246-250
Author(s):  
Ji Feng Yang ◽  
Ming Hong Lu
Keyword(s):  
Vision System ◽  
Control Unit ◽  
Two Dimensions ◽  
Intelligent Vehicle ◽  
Steering Angle ◽  
Theoretical Simulation ◽  
Automatic Mode ◽  
Driving Speed ◽  
Wide Angle Lens ◽  
Vehicle Size

Vehicle rear vision system formed by exterior mirrors and wide-angle lens plays a significant roll in the vehicle safety system. Existing vehicle rear view mirror system still has many problems unsolved, such as blind space during vehicle steering. This paper proposes a new design of intelligent vehicle rear vision system which can automatically adjust the angle of exterior vehicle mirrors in two dimensions in different driving conditions. The affecting factures includes vehicle steering angle, driving speed, and vehicle size parameters. The design uses an AT90C516RD + microcontroller as core control unit. The system adds a new automatic mode with existing manual functions remained. Upon theoretical simulation and model testing, the design has been proved to be fully functional and accepted by customer investigation with industrial potential.

scholarly journals Technical vision system for apple defects recognition: justification, development, testing

2021 ◽  
Vol 59 (4) ◽  
pp. 488-500
Author(s):  
P. P. Kazakevich ◽  
A. N. Yurin ◽  
G. А. Prokopovich
Keyword(s):  
Vision System ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Video Camera ◽  
Technological Scheme ◽  
High Productivity ◽  
Pests And Diseases ◽  
Technical Vision ◽  
Optical Module ◽  
Processing Line

The most rational method for identifying the quality of fruits is the optical method using PPE, which has the accuracy and stability of measurement, as well as distance and high productivity. The paper presents classification of fruit quality recognition systems and substantiates the design and technological scheme of the vision system for sorting them, consisting of an optical module with installed structural illumination and a video camera, an electronic control unit with an interface and actuators for the sorter and conveyor for fruits. In the course of the study, a single-stream type of fruit flow in PPE with forced rotation was substantiated, a structural and technological scheme of an STZ with a feeding conveyor, an optical module and a control unit, an algorithm for functioning of the STZ software was developed based on algorithm for segmentation of fruit colors, tracking algorithm, etc. deep learning ANN, which provide recognition of the size and color of fruits, as well as damage from mechanical stress, pests and diseases. The developed STZ has been introduced into the processing line for sorting and packing apples, LSP-4 has successfully passed preliminary tests and production tests at OJSC Ostromechevo. In the course of preliminary tests of the LSP-4 line, it was found that it provided fruit recognition with a probability of at least 95%, while the labor productivity made 2.5 t/h.

DEVELOPMENT OF A REALISTIC DRIVING BEHAVIOR BY MEANS OF FUZZY INFERENCE SYSTEM

2015 ◽  
Vol 74 (10) ◽  
Author(s):  
Nima Fouladinejad ◽  
J Mohd Taib ◽  
M K Abd Jalil
Keyword(s):  
Fuzzy Logic ◽  
Motion Control ◽  
Fuzzy Inference ◽  
Flow Simulation ◽  
Control Unit ◽  
Driving Behavior ◽  
Intelligent Vehicle ◽  
Behavior Model ◽  
Model Framework ◽  
Inference System

Realistic traffic flow simulation is possible when the vehicles inside the simulation are able to mimic human driving behavior. In view of this, this paper will discuss the implementation of fuzzy logic inside the Behavior Model framework with the intention to develop intelligent simulated vehicles. This Behavior Model consists of three different units, namely; Vision and Perception, Decision and Motion Control Unit. Vision and Perception Unit acts as the eyes for the intelligent vehicle. Decision Unit will decide the maneuvering decision. Finally, Motion Control Unit will transfer the decision into motion. However, the implementation of fuzzy logic with the integration of fuzzy rules and defuzzification techniques is done in the first and second units. This Behavior Model is controlled by two sets of fuzzy inference systems (FIS) which are free flow vehicles following and changing lanes. The finding of this research shows that the Behavior Model with fuzzy logic is able to create an intelligent vehicle that is able to self-maneuveri inside the traffic flows, realistically. 

Device for Low-Cost Assembly of Chips in RFID Inlays

2015 ◽  
Vol 237 ◽  
pp. 239-244
Author(s):  
Tomasz Samborski ◽  
Jan Wiejak ◽  
Eugeniusz Matras
Keyword(s):  
Vision System ◽  
Low Cost ◽  
Computer Control ◽  
Computer Control System ◽  
Research Activity ◽  
Commercial Activity ◽  
Security Systems ◽  
Automatic Mode ◽  
Flexible Base ◽  
Manufacturing Techniques

Security systems employ the RFID method to protect objects and data and to ensure public safety and the safety of commercial activity, e.g. through the control over technological processes. The research activity aimed at improving the level of safety of electronic and technical protection needs to be verified through the manufacture of prototype IDs with RFID inlays.The authors presents an original experimental device enabling the implementation of individual chips in RFID inlays, which were made on a flexible base to which an RFID antenna is attached. The collection of the chip from the dispenser and an unambiguous orientation of the soldered tips in relation to the antenna are provided by a four-axis vacuum manipulator cooperating with a vision system identifying the location of the chip on the manipulator and its final application area. A computer control system enables the operation in two modes, i.e. a semi-automatic mode in which it is possible to manually control the order and the way in which individual tasks are performed, and an automatic mode conducted according to the designed algorithm. The developed device is intended for research on the ways to improve the manufacturing techniques and electronic document protection by means of RFID technologies.

Trajectory Control of a Car-Like Wheeled Robot

2010 ◽  
Author(s):  
Ho-Hoon Lee
Keyword(s):  
Mobile Robot ◽  
Control Method ◽  
Trajectory Control ◽  
Design Tool ◽  
Robot Dynamics ◽  
Steering Angle ◽  
Kinematic Constraints ◽  
Kinematic Control ◽  
Driving Speed ◽  
Control Scheme

This paper proposes a trajectory control method for a carlike four-wheeled mobile robot. First, a kinematic control scheme is designed based on the nonholonomic kinematic constraints of a mobile robot, in which reference driving speed and steering angle are computed for a given desired trajectory of the robot. This kinematic control scheme, generating the reference speed and steering angle, can be applied to unmanned vehicle control with a robot driver. Second, a new backstepping trajectory control scheme is designed based on the robot dynamics subject to the nonholonomic kinematic constraints, in which the desired driving force and steering torque are computed for a given desired trajectory. In this study, the Lyapunov stability theorem is used as a mathematical design tool. The proposed control guarantees asymptotic stability of the trajectory control while keeping all internal signals bounded. Finally, the validity of the theoretical results is shown by realistic computer simulations with one sampling delay in the control loop.

scholarly journals An Autonomous Grape-Harvester Robot: Integrated System Architecture

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1056
Author(s):  
Eleni Vrochidou ◽  
Konstantinos Tziridis ◽  
Alexandros Nikolaou ◽  
Theofanis Kalampokas ◽  
George A. Papakostas ◽  
...  
Keyword(s):  
Remote Control ◽  
System Architecture ◽  
Industry 4.0 ◽  
Modular Design ◽  
Vision System ◽  
Control Unit ◽  
Integrated System ◽  
Specific Design ◽  
Sensing System ◽  
Seasonal Labor

This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdependent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely harvest, green harvest and defoliation. We present an overview of the multi-purpose overall system, the specific design of each unit of the system and the integration of all subsystems. In addition, the fully sensory-based sensing system architecture and the underlying vision system are analyzed. Due to its modular design, the proposed system can be extended to a variety of different crops and/or orchards.

scholarly journals Development control system robotic platform for horticulture

2021 ◽  
Vol 262 ◽  
pp. 01024
Author(s):  
Dmitriy Khort ◽  
Alexey Kutyrev ◽  
Rostislav Filippov ◽  
Stepan Semichev
Keyword(s):  
Control System ◽  
Vision System ◽  
Control Unit ◽  
Weather Conditions ◽  
Robotic Platform ◽  
Satellite Navigation System ◽  
Central Computer ◽  
Electronic Control System ◽  
Robotic Vehicle ◽  
Differential Control

The article presents a control system for a robotic platform for horticulture. The electronic control system consists of a running engine control unit, a stepper motor steering unit, an electronic differential control unit, a power plant automatic on / off control unit, and battery charging balancing. The developed control system of the robotic vehicle contains a central computer that collects information from sensors and sensors, processes it and transmits control signals to the drives of the machine movement. The movement of the robotic platform is carried out both by a radio signal with a remote control, and in offline mode on a pre-set map of the area according to data from the GLONASS/GPS differential receiver of the satellite navigation system. It is also possible to independently control the movement of a robotic platform using a vision system. The autonomy of the robotic platform provides 10 hours of continuous operation in low-light conditions in various weather conditions.

scholarly journals Real Time Vision System for Autonomous Vehicles

2020 ◽  
Vol 7 (4) ◽  
pp. 87-90
Author(s):  
Padmasree L ◽  
Preethi Eluri ◽  
Sai Subrahmanya Akhil Badampudi ◽  
Sreedhar Reddy Mukkamalla
Keyword(s):  
Visual Perception ◽  
Vision System ◽  
Tracking System ◽  
Path Tracking ◽  
Lane Detection ◽  
Curvature Radius ◽  
Steering Control ◽  
Steering Angle ◽  
Control Subsystem ◽  
Distance Measuring

With the increase in vehicle accidents regularly, there is a need to control these accidents and save precious lives. The main reason for accidents on roads are mainly observed by driver misconception, recklessness and over speeding. So, there is a need to develop a Vision system which has a ability to explore its surroundings and move accordingly. The Vision system is divided into 3 subsystems as Visual perception subsystem, Brake and Acceleration subsystem and Steering control subsystem. The Visual perception means the ability to interpret surrounding environment using light in the visual spectrum reflected by the objects in the environment. This subsystem uses distance measuring sensors such as Light Detection and Ranging (LiDAR) and Ultrasonic sensors for detecting objects and sends the data to brake and acceleration subsystem using Arduino IDE software. According to the data received either the brake or acceleration is initiated, it means that when the distance measuring sensor values reach the threshold values then the brakes are applied or else acceleration is implemented. In order to have a smooth ride the acceleration should be uniform without any jerks though speed changes. This is resolved by using Proportional-Integral- Derivative (PID) controller which reduces the gradual difference between the desired and input speed. The Steering control subsystem involves lane detection and path tracking. The lane detection is done using Python and OpenCv which uses various image processing steps, gives the steering angle by calculating the curvature radius of lanes. Therefore path tracking system is initialized taking the steering angle and direction as input for controlling the position of the vehicle.

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