Compact image processing based kin recognition, distance measurement and identification method in a robot swarm

Bolla, Kálmán; Kovács, Tamás; Fazekas, Gábor

doi:10.1109/icccyb.2010.5491237

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…While this connection between robots can be provided by Wi-Fi networks and GPS systems, the unreliability of these networks and the amount of communicated data that grows gradually with the number of robots in the team keeps the need for an alternative solution based on local sensing. Several studies have dealt with this subject using a variety of sensors and methods [20,21,22], but there are few studies performing kin detection using lidar [23,24,25]. For this reason, as a case study for our new robot, we proposed a new geometric kin detection method and tested it with two different scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…Many collective behaviors tasks such as aggregation [27], pattern formation [28] and flocking [29] have been developed using simulators but regardless of the type of task required, detecting surrounding objects in the environment and distinguishing robots from these objects are essential for robot's operations. Several studies have proposed solutions to handle this issue which is often called "kin detection task" using different types of sensor like, infrared [20], ultrasound [21], vision [22], and Lidar [23,24,25] sensors. Lidar-based studies use a common approach which depends on using circular robots or any circular object attached to the robot (As in our case where we use a circular-shaped Lidar) and then attempting to detect these robots by taking advantage of circularity and known diameter of these robots or objects attached to them.…”

Section: Robot Namementioning

confidence: 99%

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Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Yilmaz¹,

Bayındır²

2019

European Journal of Science and Technology

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In the last few decades, the robotics world has seen great progress at all levels, from personal assistant robots to multi-robotic and intelligent swarm systems. Simulation platforms play a critical role in this improvement due to efficiency, flexibility, and fault tolerance they provide during the cycles of developing and testing new strategies and algorithms. In this paper, we model a new mobile robot equipped with a 2D Lidar using Robot Operating System (ROS) and use this robot model to develop a robot detection method in Gazebo simulation environment. Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in multi-robot and swarm robotic applications. In this paper, we use Lidar to handle this task by applying the following steps: (1) acquisition of laser data and pre-processing, (2) segmentation of data using the point-distance-based segmentation method, (3) classification of segments by applying two levels of filtering: filtering by segment diameter which aims to eliminate segments that don't fit a certain size (Lidar size) using features for each segment, filtering by segment shape to check remaining segments to test if they fit the Lidar's shape (which is a circle with known radius) or not by using the circle fitting method and (4) identify the position of kin relative to the observer robot. Two different scenarios are discussed in the experiments section. In the first scenario, many cylindrical objects with radius different from the robot's Lidar were used in addition to a robot, and thus objects are distinguished from the robot in the first level of filtering without using the second one which may be a complex operation. In the second scenario, various objects with a similar radius were used, and due to the similarity in the radius between the Lidar and the objects, it was necessary to apply all the method's steps to detect the kin robots. It was noticed from experiments that the accuracy of the results depends on two main factors: the distance between the observer robot and other objects or robots and the amount of noise in the Lidar measurements.

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Section: Discussionmentioning

confidence: 99%

Section: Robot Namementioning

confidence: 99%

Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Yilmaz¹,

Bayındır²

2019

European Journal of Science and Technology

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“…As an example of using non-IR-based systems, Bolla et al [8] developed a visual kin recognition and localization method that can detect and identify kin robots in a swarm robotic system. The main idea of the suggested method depends on using the Fast Fourier Transform (FFT) to extract the peak in the FFT spectrum related to the zebra pattern used to distinguish robots in the swarm.…”

Section: Lidar-based Robot Detection and Positioning Using Machine Le...mentioning

confidence: 99%

Lidar-based Robot Detection and Positioning using Machine Learning Methods

YILMAZ¹,

Bayındır

2022

Balkan Journal of Electrical and Computer Engineering

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This paper presents a machine learning-based kin detection method for multi-robotic and swarm systems. Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in most of the multi-robotic applications. While infrared, ultrasonic, vision systems had been mainly used for applying the robot detection and relative positioning task in the literature, studies that use the Lidar-based approach is limited. The proposed method uses the Lidar sensor to discover the work area and determine the distance and the angle of all kin members relative to the observer robot. The main steps of the proposed method can be summarized as follows: 1) the Lidar distance points are read and stored as a vector with some pre-processing, 2) the acquired distance points representing different objects in the environment are separated from each other using a segmentation method, 3) in order to classify the segmented objects, the segment classification process starts with extracting five features for each object, then these features are fed to various machine learning classification algorithms to distinguish the kin robots, 4) the segments classified as a kin robot in the previous step are handled and the relative position is found for each of them. A new mobile robot prototype has been modeled and equipped with a Lidar sensor using ROS platform. Lidar has been used to collect data and four different classification methods have been tested to verify the efficiency of the method using Gazebo simulation platform.

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“…Swarm" [5], by K. Bolla, T. Kovacs, Kecskemet College, Izsaki ut 10, H-6000 Kecskemet, Hungary.2010 IEEE presents that there are a reliable visual recognition, distance measurement and identification algorithm to detect and identify kin robots in a mobile-robot swarm. Every robot in the swarm is equipped with a zebra pattern and our idea is based on the Fast Fourier Transform (FFT), which has a relatively low complexity.…”

Section: "Compact Image Processing Based Kin Recognition Distance Mementioning

confidence: 99%

Android Application Implemented for Distance Measurement using Image Processing

Ashok¹,

Jadhavar²,

R.M³

2017

International Journal of Advanced Research in Computer and Comm

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Now a day's multiple images special photographing poses methods are used to measure the distance from the real world, these methods are able to measure the height with a special view configuration but not enough to measure the depth, ground distance from the real time. We propose a novel image-based method that can measure various types of distance from a single image captured by a smart mobile device. The embedded accelerometer is used to determine the view orientation of the device. In this system pixels can be back-projected to the ground; there are many efficient calibration methods using two known distances. Then the distance in a pixel is transformed to a real distance in centimeter with a linear model parameterized by the magnification ratio. Various types of distance specified in the image can be computed accordingly.

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Compact image processing based kin recognition, distance measurement and identification method in a robot swarm

Cited by 5 publications

References 5 publications

Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Lidar-based Robot Detection and Positioning using Machine Learning Methods

Android Application Implemented for Distance Measurement using Image Processing

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