Centralized approach for multi-node localization and identification

Hasan, Ola A.; Ali, Ramzy S.; Rashid, Abdulmuttalib T.

doi:10.37917/ijeee.12.2.8

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…It is applied with wired or wireless devices, such as mobile phones, TV's, PDAs, printers, etc. [5]. In the 1990s, the first indoor localization systems at AT&T Cambridge were fitted with symbolic location information to every active badge, such as the area where the active badge is and an area in a building.…”

Section: Related Workmentioning

confidence: 99%

IR Sensors Array for Robots Localization Using K Means Clustering Algorithm

AL-Furati

Rashid

Al-Ibadi

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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the localization problem is resolved by using an array of IR receiver sensors distributed uniformly in the environment. the position of a multi-robot system in an indoor localization system is successfully estimated using a new algorithm. The necessary information for the localization development is collected by scanning the IR sensor array in the environment. The scanning process is done column by column to recognize and state the position of the IR receiver's sensors, which receive signals from the IR transmitter that is fixed on the robot. This principle of scanning helps to minimize the required time for robot localization. The kmeans clustering algorithm is used to estimate the multi-robot's locations by isolating the labeled IR receivers into clusters. Basically, the multi-robot position is estimated to be in the middle of each cluster. Simulation results demonstrate the advanced algorithm in estimation to the multi-robot positions for various dimensional IR receiver's array.

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Section: Related Workmentioning

confidence: 99%

IR Sensors Array for Robots Localization Using K Means Clustering Algorithm

AL-Furati

Rashid

Al-Ibadi

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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“…This process also needs to deal with obstacle avoidance and collision avoidance, when robots move to their final location. Different localization approaches have been designed to work in local and global knowledge environments with different types of range sensors, such as IR sensor, ultrasonic sensor, sonar and laser range finders [5][6][7][8][9]. These localization approaches are very important to perform the formation of multi-robot, since they are produce the initial position and orientation of each robot in environment.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Multi-mobile Robot Formation Control

Bayadir¹,

Abdulmuttalib²

2019

IJCA

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This paper denotes a survey on formation control of multimobile robot systems which drawn significant attention for the last years. It is concentrated on the stability of multi mobile robots when they obtained the required formation. Also this paper discusses the approaches of formation control and applications of them in changing and remote environments. Two classifications for the formation control methods are surveyed in this paper: the formation control strategies and the formation control stability. The differences among the surveyed approaches are discussed and the results are summarized.

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“…Since most of the applications for wireless sensor networks and multi-robot system need to know their initial positions to perform important tasks, a great attention given to the design efficient algorithms for localization. The term localization means that to find the location of objects in an environment [6]. This process is done by manipulating the information received from the sensors and sends it to the infrastructure to compute the location of the objects.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Locations Matching Algorithm for Multi-Object Recognition and Localization

Rashid¹,

Zayer²,

Rashid³

2018

IJEEE

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A new algorithm for multi-object recognition and localization is introduced in this paper. This algorithm deals with objects which have different reflectivity factors and distinguish color with respect to the other objects. Two beacons scan multi-color objects using long distance IR sensors to estimate their absolute locations. These two beacon nodes are placed at two corners of the environment. The recognition of these objects is estimated by matching the locations of each object with respect to the two beacons. A look-up table contains the distances information about different color objects is used to convert the reading of the long distance IR sensor from voltage to distance units. The locations of invisible objects are computed by using absolute locations of invisible objects method. The performance of introduced algorithm is tested with several experimental scenarios that implemented on color objects.

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Centralized approach for multi-node localization and identification

Cited by 8 publications

References 6 publications

IR Sensors Array for Robots Localization Using K Means Clustering Algorithm

IR Sensors Array for Robots Localization Using K Means Clustering Algorithm

A Survey of Multi-mobile Robot Formation Control

Design and Implementation of Locations Matching Algorithm for Multi-Object Recognition and Localization

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