A practical performance analysis of low-cost sensors for indoor localization of multi-node systems

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

doi:10.1109/itecha.2017.8101955

Cited by 11 publications

(9 citation statements)

References 6 publications

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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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Section: Introductionmentioning

confidence: 99%

A Survey of Multi-mobile Robot Formation Control

Bayadir¹,

Abdulmuttalib²

2019

IJCA

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“…Extraordinary consideration is given to structure effective calculations for localization. The term localization implies that to discover the pose (position and orientation) of robots in an environment according to the information obtained from sensors equipped with each one [6][7][8]. Localization can be grouped into two kinds: absolute localization, in which every robot tries to decide its pose as for some global coordinate system, and relative localization, in which every robot tries to decide the pose of each other robots in the group, with respect to itself [9][10].…”

Section: Introductionmentioning

confidence: 99%

RP Lidar Sensor for Multi-Robot Localization using Leader Follower Algorithm

Issa¹,

Rashid²

2019

IJEEE

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In this paper, a new technique for multi-robot localization in an unknown environment, called the leader-follower localization algorithm is presented. The framework utilized here is one robot that goes about as a leader and different robots are considered as followers distributed randomly in the environment. Every robot equipped with RP lidar sensors to scan the environment and gather information about every robot. This information utilized by the leader to distinguish and confine every robot in the environment. The issue of not noticeable robots is solved by contrasting their distances with the leader. Moreover, the equivalent distance robot issue is unraveled by utilizing the permutation algorithm. Several simulation scenarios with different positions and orientations are implemented on (3-7) robots to show the performance of the introduced technique.

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“…Design and Implementation of Locations Matching Algorithm for Multi-Object Recognition and Localization measurements for the intensity of reflected light by objects depending on the surface type of objects, like surfaces having different colors and different level of reflectivity. Objects features can be determined by these measurements [12][13][14]. In another work, a study on the effect of glittering and reflective objects with different colors is suggested.…”

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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A practical performance analysis of low-cost sensors for indoor localization of multi-node systems

Cited by 11 publications

References 6 publications

A Survey of Multi-mobile Robot Formation Control

A Survey of Multi-mobile Robot Formation Control

RP Lidar Sensor for Multi-Robot Localization using Leader Follower Algorithm

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

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