Robotic navigation algorithm with machine vision

Pachón, César G.; Enciso-Aragon, Carlos J.; Moreno, Róbinson Jiménez

doi:10.11591/ijece.v10i2.pp1308-1316

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…It is very important to provide enough of the information on the location of the robot to move. That is why, the methods of the localization have been considered key to navigation process [49]. In addition, navigating a moving robot requires additional skills, including control capabilities, trajectory planning, obstacle avoidance, and safe distances to the goal, which mobile robots need to perform to provide superior navigation.…”

Section: Navigation Systemmentioning

confidence: 99%

A one decade survey of autonomous mobile robot systems

Zghair

Al-Araji

2021

IJECE

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<span lang="EN-US">Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.</span>

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Section: Navigation Systemmentioning

confidence: 99%

A one decade survey of autonomous mobile robot systems

Zghair

Al-Araji

2021

IJECE

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“…Regularly, the competence of sophisticated image processing techniques [1]- [4], such as super resolution [5], remote sensing [6], and medical imaging [7]- [9], are definitely susceptible from noise thereupon noise suppressing technique [10]- [20] are become an irresistible momentous process. Theoretically, the noise suppressing technique regularly constructs the undesirable effect such as blurring effect or detail losing thereupon the fundamental intention of noise suppressing technique is for concealing noise from noisy photograph whereas protecting detail.…”

Section: Liturature Reviewmentioning

confidence: 99%

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Patanavijit

Thakulsukanant

2021

IJEECS

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Due to the extreme insistence for digital image processing, plentiful modern noise suppressing techniques are embodied of dissimilarity process and suppressing process. One of the extreme capability dissimilarity is hard decision threshold (HDT) dissimilarity, which has been recently declared in 2012, for suppressing the impulsive noisy photographs thus the computer experimental statement attempts to investigate the capability of the noise suppressing technique that is stand on HDT dissimilarity for the processed photographs, which are corrupted by fixed-intensity impulse noise (FIIN). This paper proposes the noise suppressing technique stand on HDT dissimilarity for FIIN. There are 3 primary contributions of this paper. The first contribution is the statistical average of the HDT dissimilarity of noise-free elements, which are computed from plentiful ground-truth photographs by varying window size for the best HDT window size. The second contribution is the statistical average of the HDT dissimilarity of corrupted elements, which are computed from plentiful corrupted photographs by varying outlier density for the best HDT window size. The final contribution is the statistical interrelation of the capability of the noise suppressing technique and hard consistent of HDT dissimilarity are investigated by varying the outlier denseness for the best HDT hard consistence.

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“…In the last three decades, many algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] that hinge on distinctive techniques have been investigated and evolved for noise apprehension and noise dissolving [10][11][12][13][14][15][16][17][18][19][20] in order to restore profaned portraits because there are great requirements for high quality portraits, which have been used in modern complex applications [1][2][3][4] such as medical imaging [7][8][9], remote sensing [6], super resolution (SR) [5],…”

Section: Sivve Noisementioning

confidence: 99%

Simulation Inquisition of Noise Dissolving Algorithm Hinge on TTSD filter for RIIN Situation

Patanavijit

Thakulsukanant

2021

ECTI-CIT

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This primary aim of this philosopher paper investigates the efficacy of the noise dissolving algorithm hinge on TTSD (Triple Threshold Statistical Detection) filter that has been originated since 2018 is one of the highest efficacy for dissolving RIIN (Random-Intensity Impulse Noise), exclusively at dense distribution. As a results, there are three essential contributions: the exhaustive explanation of the TTSD filter algorithm and its computation examples, the calculation simulation of noise apprehension correctness and overall comparative simulation of noise dissolving effectiveness. For TTSD filter, three malleable offsets that are the complementary requirement are employed in the TTSD filter that can adequately resolve the limitation of the antecedent noise dissolving algorithms. The first malleable offset is calculated for determining the noise characteristic of all elements by using the mathematical verification. Next, the second malleable offset is calculated for determining the another noise characteristic by using the normal distribution mathematical verification (the average value and standard deviation value). Later, the third malleable offset is calculated for determining the another noise characteristic by using the quartile mathematical verification (median value). In the simulation inquisition, the bountiful standard portraits that are desecrated by RIIN (Random Intensity Impulse Noise) with many dense distributions are experimented by noise dissolving algorithm hinge on TTSD in both noise segregation and noise dissolving perspective.

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Robotic navigation algorithm with machine vision

Cited by 9 publications

References 17 publications

A one decade survey of autonomous mobile robot systems

A one decade survey of autonomous mobile robot systems

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Simulation Inquisition of Noise Dissolving Algorithm Hinge on TTSD filter for RIIN Situation

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