Development of A Color Tracking Control System for Vehicle Navigation (original) (raw)
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A vehicle implementation of a color following system using the CMUcam3
2009
By creating a center of mass for a specific color, color tracking, and controlling servos relative to the distance from the center of mass, the CMUcam3 and Gears SMP robotics platform can be used to build a color tracking system to autonomously track a certain color within the Y'CrCb color range. Using pulse-width modulation arrays to control the servomechanic DC motors, a control system based on mean image data can be implemented to steer and direct an autonomous CMUcam3 driven vehicle platform.
Autonomous Vehicle Control using Image Processing
International Journal for Research in Applied Science & Engineering Technology, 2021
A significant perspective related with vehicles is their speed. A faster vehicle encourages us to arrive at our destination in lesser time, sparing our valuable time. In any case, tragically, we have been seeing the ascent in vehicular mishaps because of uncontrolled speeding by the drivers. The traffic signs alone cannot ensure the safety of vehicles since it is dependent upon the driver to adhere to the directions. Likewise, there is the situation of human-mistake where the driver essentially neglects to pay special mind to the traffic signs. This paper focuses on the road traffic sign detection systems which help in informing the intelligent vehicle about the possible road conditions ahead and be cautious about it with the help of Image processing. A module which consists of a Raspberry Pi or USB camera with a wide view and a simple processor is installed on the vehicle. The developed system works with three different stages: image pre-processing, detection, and recognition. The entire developed system is programmed using Python incorporated with OpenCV library and implemented using open source hardware platform and open-source software environment.
A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads
Autonomous Robots, 2004
This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.
This article presents the results of experiments on path planning and control of automated guided vehicles (AGV) using single, fixed ceiling mounted, monocular cameras and colored markers. The camera employed in the system serves as both a sensor and controller. Initially, the working environment is structured using colored markers for given applications. For every new setup, structuring the environment is essential. The image processing algorithm identifies the colored markers and their positions, which are then utilized for path planning and segmentation. The actuation time required to transverse each segment is calculated and then AGV is actuated accordingly. A transformation or inverse mapping matrix (M), predetermined, is employed for calculating world coordinates from given image coordinates. Path planning and AGV control are across various paths, both with and without static obstacles, in real-time applications. The colored marker detection and recognition accuracy for the given setup have been found cent percentage while the AGV reaches the goal point with an error margin of around 3.9% on straight paths, both with and without obstacles.
Image-Based Driver's Guidance System
The paper presents a low-cost navigation system, which is based on digital terrestrial images. The database of the test area is filled by digital images about road junctions, crossings, and other "irregularities" taken with a high-resolution commercial camera. The developed guidance system solves the usual problem within urban circumstances (one or two way streets etc.), which is followed by a systematic image query along the found optimal path. The queried images of the database are extended by simple navigational instructions (arrows) to give visual information for the driver. The obtained image-based itinerary contains more information than the widely used navigation systems, and allows better navigation performance in unknown situations. The paper gives an overview about the planning of the field works, the execution of data acquisition by GPS and photography, as well as the database built-up and how the whole data set is structured and stored. It is followed by the pr...
Vehicle Control Using Raspberry pi and Image Processing
Intelligent Communication, Control and Devices, 2018
The objective of the proposed work is to implement the available technique to detect the stop board and red traffic signal for an autonomous car that takes action according to traffic signal with the help of raspberry pi3 board. The system also uses ultrasonic sensor for distance measurement for the purpose of speed control of vehicle to avoid collision with ahead vehicle. Rpi camera module is ued for signboard detection and ultrasonic sensors are used to get the distance information from the real world.The proposed system will get the image of the real world from the camera and then masking and contour techniques are used to detect the red signals of the traffic and To determine the traffic board signs like stop board system will use haar cascade technique to determine the stop words.So car will be able to take action and reduces the chances of human errors like driver mistakes that results road accidents .The coding for this whole system is in python and for image processing opencv is used that is much efficient as compare to the matlab .Ultrasonic sensor is used for the obstacle detection in place of camera because distance finding from the camera is more complex and computational as compare to the ultrasonic sensor. Ultrasonic sensor directly gives the obstacle distance infront of it without more complex computations.
Implementation of Vision-Based Trajectory Control for Autonomous Vehicles
Journal of Engineering Science and Military Technologies
This paper demonstrates building, implementing, and developing a trajectory tracking control system based on computer vision for autonomous vehicles. The main goal of this system is to enforce the autonomous vehicle to be able to track road lane. This system includes a single digital camera, an embedded computer, and a microcontroller board. The digital camera is mounted at the top of the vehicle along its longitudinal axis. It captures a real-time sequence of images during vehicle motion. The captured images are then processed using Open-CV library for Python compiler over Linux operating system. These software packages are running on the embedded computer (Raspberry Pi 2) to obtain geometrical data of road lane. From this data, the observable errors can be determined. These errors are vehicle lateral offset and a heading error. Finally, a steering controller utilizes these errors in control law to compute the steering command. This command corrects offset and heading errors to ensure that the vehicle is in its way. The embedded computer then paths this command to Arduino microcontroller board to adjust the steering servomotor. The proposed implementation also demonstrates the integration between the embedded computer and microcontroller using Ethernet. During this work, a set of autonomous driving experiments is performed. Significant results are obtained that demonstrate the accuracy and robustness of the lane detection and control algorithms.
2019 Innovations in Power and Advanced Computing Technologies (i-PACT), 2019
In this paper, the hardware setup of an autonomous robotic vehicle is developed. The controller of the vehicle is developed based on the computer vision technology which became more smart due to the application of popular tool open CV. An algorithm based on color detection is developed to navigate the vehicle in different directions. Raspberry Pi camera is used to access images which are the indications of the directions of the movements such as stop, left turn, right turn. The efficacy of the proposed algorithm is verified experimentally.
A panoramic color vision system for following ill-structured roads
2006
The ability to follow ill-structured roads, such as footpaths, dirt tracks and corridors, is important for mobile robot navigation. This paper presents a panoramic colour vision based road following system for ill-structured roads. Roads are modeled with rapidly adapting 3D colour histograms and a simple yet generic geometric model. The computational complexity of the geometric model fitting stage has been significantly reduced compared to other works. A Kalman filter is used to smooth out any measurement noise. Results from experiments in tracking a footpath demonstrate the robustness of the system.
Vehicle Tracking Using Image Processing Techniques
Lecture Notes in Computer Science, 2004
Today, unmanned vehicle technologies are developing in parallel with increasing interest in technological developments. These developments aim to improve people's quality of life. Transportation, which is a part of human life, has taken its share from this developing technology. With the development of artificial intelligence, it is aimed to provide the necessary assistance to the driver in transportation and to provide ease of driving. This development has been increased with ADAS (Advanced Driver Assistance Systems) in vehicles, but it is not possible to experience a completely driverless and comfortable road. With all these demands and conditions, autonomous vehicles have quickly attracted attention. While ADAS is a warning system, all accident risks that may arise from the driver rather than the warning to the driver in autonomous vehicles are minimized by the vehicle. In this paper, we present an autonomous vehicle prototype that follows lanes via image processing techniques, which are a major part of autonomous vehicle technology. Autonomous movement capability is provided by using various image processing algorithms such as canny edge detection, Sobel filter, etc. We implemented and tested these algorithms on the vehicle. The vehicle detected and followed the determined lanes. By that way, it went to the destination successfully.