Vision based navigation for golf ball collecting mobile robot

Yun, Cheol Ho; Moon, Yong-Seon; Ko, Nak Yong

doi:10.1109/iccas.2013.6703893

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…Másrendszerek fedélzeti kamerát [3], lézeres távolságmérő (LIDAR) szenzort [4] vagy egyéb külső megfigyelő rendszert [5] alkalmaznak. A robot fedélzeti számítógépén a navigáláshoz megbíz-ható megoldás szükséges, amely még korlátozott számításikapacitású robotokon is megvalósítható.…”

Section: Bevezetőunclassified

MARG szenzor alapú pozíció becslés periodikus mozgásra

Nagy

2024

RIiM

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A kutatás során magnetométer, giroszkóp és gyorsulásérzékelő szenzorok (MARG szenzor) jeleinek fúzióját vizsgáltuk. A cikk fókusza a felügyelt tanulás által végzett szenzorfúzió, amely a különböző típusú mért adatok párhuzamosfeldolgozásán alapuló pozíció becslését jelenti periodikus mozgások esetén. A tanuló algoritmusok alkalmazhatóságát egy inga gyakorlati példáján keresztülvizsgáltuk meg

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Section: Bevezetőunclassified

MARG szenzor alapú pozíció becslés periodikus mozgásra

Nagy

2024

RIiM

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“…Acceleration data are, however, heavily affected by noise and the integration of measurement noise causes a shift in the position calculation and ruins the prediction. The noise affected relations are defined by Equations (2)- (4).…”

Section: Problem Statementmentioning

confidence: 99%

“…The navigation system is an important part of an autonomous mobile robot. There are numerous practical applications which use global positioning system (GPS) [1,2], onboard camera [3,4], light detection and ranging (LIDAR) sensor [5,6] or other external observation system [7,8].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Angular Rate and Gravity Sensor Based Supervised Learning for Positioning Tasks

Nagy

Botzheim

Köröndi

2019

Sensors

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This paper deals with sensor fusion of magnetic, angular rate and gravity sensor (MARG). The main contribution of this paper is the sensor fusion performed by supervised learning, which means parallel processing of the different kinds of measured data and estimating the position in periodic and non-periodic cases. During the learning phase, the position estimated by sensor fusion is compared with position data of a motion capture system. The main challenge is avoiding the error caused by the implicit integral calculation of MARG. There are several filter based signal processing methods for disturbance and noise estimation, which are calculated for each sensor separately. These classical methods can be used for disturbance and noise reduction and extracting hidden information from it as well. This paper examines the different types of noises and proposes a machine learning-based method for calculation of position and orientation directly from nine separate sensors. This method includes the disturbance and noise reduction in addition to sensor fusion. The proposed method was validated by experiments which provided promising results on periodic and translational motion as well.

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“…map-based, map building, and maples navigation. One of map-based navigation method was described in [14] that uses a stereo camera on a wheeled robot as a golf ball collector. It's associated with a wide-angle camera mounted on top of the golf course with a viewing area covers area 20m x 20m, with a viewing angle of 80 vertical and horizontal viewing angle of 55 with the camera installation height is 7m above the ground.…”

Section: Introductionmentioning

confidence: 99%

“…Resolution capable captured by this camera is equal to 1280x780 pixels. The camera catches the image is then processed by a computer server which then builds navigation map grid-shaped or there is a call later with questions of occupancy (occupancy folder) that can indicate where to position the robot makers ball on the golf course, and where the ball position to be taken off the map, is determined by the server and the robot can move to a certain position based on the information sent wirelessly [14], [15]. The next category of navigation method is map building navigation described in [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Distance Estimation based on Color-Block: A Simple Big-O Analysis

Rahmani¹,

Aprilianto²,

Ismanto³

et al. 2017

IJECE

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This paper explains how the process of reading the data object detection results with a certain color. In this case, the object is an orange tennis ball. We use a Pixy CMUcam5 connecting to the Arduino Nano with microcontroller ATmega328-based. Then through the USB port, data from Arduino nano re-read and displayed. It's to ensure weather an orange object is detected or not. By this process, it will be exactly known how many blocks object detected, including the X and Y coordinates of the object. Finally, it will be explained the complexity of the algorithms used in the process of reading the results of the detection orange object.

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Vision based navigation for golf ball collecting mobile robot

Cited by 11 publications

References 4 publications

MARG szenzor alapú pozíció becslés periodikus mozgásra

MARG szenzor alapú pozíció becslés periodikus mozgásra

Magnetic Angular Rate and Gravity Sensor Based Supervised Learning for Positioning Tasks

Distance Estimation based on Color-Block: A Simple Big-O Analysis

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