Smooth Track-Keeping and Real Time Obstacle Detection Algorithm and its PID Controller Implementation for an Automated Wheeled Line Following Robot

2011 ◽  
Vol 201-203 ◽  
pp. 1966-1971 ◽  
Author(s):  
Mohammad Rokonuzzaman ◽  
Shah Muhammad Ferdous ◽  
Enaiyat Ghani Ovy ◽  
Md. Ashraful Hoque
Keyword(s):  
Mobile Robot ◽  
Pid Controller ◽  
Material Handling ◽  
Moving Objects ◽  
Detection Algorithm ◽  
Acute Angle ◽  
Obstacle Detection ◽  
Response Position ◽  
Wavy Line ◽  
Line Following

Line following automated robots is extensively used in industries for smooth running of production. This paper presents a simple and effective solution for path tracking problem for a wheeled automated mobile robot which can be used for material handling in industries. A PID controller has been used for controlling the robot which is capable of moving safely by smooth track-keeping in partially structured environment without any collision with static or moving objects. The purpose of the project is to build a mobile robot which will provide fast, smooth, accurate and safe movement in any given line or track. A straight or wavy line would be simple to follow whereas aT-junction, 90 degree bends, acute angle bends and grid junctions would be difficult to navigate through. This is due to the physical kinematics constraints which are limited to motor response, position and turning radius of the robot. A line sensor configuration has been proposed to improve the navigation reliability of the mobile robot which uses differential drive system. A dynamic algorithm has been developed for detecting and following a specified line which ensures the reliable and safe movement of the robot.

A New Roadside Detection Method Based on Obstacle Detection

2012 ◽  
Vol 429 ◽  
pp. 324-328
Author(s):  
Chun He Yu ◽  
Dan Ping Zhang ◽  
Rui Guo
Keyword(s):  
Mobile Robot ◽  
High Stability ◽  
Detection Method ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Laser Radar ◽  
The Road ◽  
Straight Lines

In order to provide road information for outdoor mobile robot in a complicated environment, a new roadside detection method is proposed based on obstacle detection by applying a four-layer laser radar LD_ML. Because roadside obstacles distribute alone a road, theirs fitting straight lines are parallel to the road. The roadsides detection algorithm includes four steps: first, judge if there are obstacles along roadside or not; second, extract obstacles which belong to roadsides; third, build fitting straight lines through the roadside obstacles; at last, in order to obtain steady and precise roadsides, a EKF method is performed to track the roadsides. The results of experiment have testified the road roadsides detection algorithm has high stability and reliability.

Consecutive Scanning Scheme: Application to Localization and Dynamic Obstacle Detection for a Mobile Robot

2002 ◽  
Author(s):  
Jae-Yong Lee ◽  
Sooyong Lee
Keyword(s):  
Mobile Robot ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Data Sets ◽  
Data Set ◽  
Characteristic Points ◽  
Scanning Scheme ◽  
Mobile Robot Localization ◽  
Robot Configuration ◽  
Dynamic Obstacle

This paper presents the mobile robot localization and obstacle detection algorithm using the consecutive range sensor scanning scheme. For known environment, a mobile robot scans the environment using which can rotate 360°. The environment is rebuild using characteristic points which means nodes of two adjacent walls, and obstacle is detected by comparing characteristic points of both original environment and scanned data set. If scanning is done densely for a certain position, inaccuracy of sensor can be overcome to some extent. It is very useful algorithm in detecting the moving obstacle. By comparing two data sets, the movement of an obstacle can be picked out. Furthermore, consecutive scanning data set provides additional localization information so that we can get the robot configuration more precisely.

scholarly journals Cellular neural networks for motion estimation and obstacle detection

2003 ◽  
Vol 1 ◽  
pp. 143-147 ◽  
Author(s):  
D. Feiden ◽  
R. Tetzlaff
Keyword(s):  
Neural Networks ◽  
Motion Estimation ◽  
Video Processing ◽  
Moving Objects ◽  
Displacement Vector ◽  
Detection Algorithm ◽  
Cellular Neural Networks ◽  
Obstacle Detection ◽  
Real Time Processing ◽  
Processing Steps

Abstract. Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN). It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible.

A Fast Obstacle Detection Algorithm for Mobile Robots and Its Application

ROBOT ◽  
2011 ◽  
Vol 33 (2) ◽  
pp. 198-201 ◽  
Author(s):  
Xiaochuan ZHAO ◽  
Peizhi LIU ◽  
Min ZHANG ◽  
Lihui YANG ◽  
Jianchang SHI
Keyword(s):  
Mobile Robots ◽  
Detection Algorithm ◽  
Obstacle Detection

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

Line following for a mobile robot

1995 ◽  
Author(s):  
Bradley O. Matthews ◽  
Michael A. Ruthemeyer ◽  
David Perdue ◽  
Ernest L. Hall
Keyword(s):  
Mobile Robot ◽  
Line Following

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

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