Formation control and coordination of swarm robotic systems

Kyzyrkanov, Abzal; Atanov, Sabyrzhan; Aljawarneh, Shadi

doi:10.1145/3492547.3492704

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…There are a large number of different sensors available for making measurements [56], [57]. Sensors are widely used in various fields, such as scientific research, testing, quality control, automated control systems, and others [58]- [60]. As part of the development of a prototype gas analyzer designed to determine the concentration of multicomponent gas mixtures in air in laboratory and industrial conditions, the AS7265x and BMP180 sensors were used.…”

Section: B Development Of a Prototype Gas Analyzer For Data Collectionmentioning

confidence: 99%

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

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The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures.

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Section: B Development Of a Prototype Gas Analyzer For Data Collectionmentioning

confidence: 99%

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

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“…But it does not address issues related to the scalability and reliability of the system. Another approach is a behavioral approach, as described in [6], which coordinates the motion of multi-agent robotic systems to maintain a certain geometric pattern. This paper does not consider how the system must react if an obstacle appears in the way.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Furthermore, optimization using genetic algorithms can be computationally expensive and not suitable for large groups of robots or more complex tasks. In our algorithm, the motion of the multi-agent system turns on the lidars only as necessary, reducing energy consumption, whereas all the above-mentioned formation control algorithms [4][5][6][7][8][9][10] require turning on the sensors of all robots during motion.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of control software for self-organizing intelligent mobile robots

Toibazarov¹,

Baiseitov

Kyzyrkanov

et al. 2023

EEJET

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Maintaining a specific geometric formation during the movement is crucial for multiagent systems of mobile robots in various applications. Proper coordination can lead to reduced system costs, increased reliability and efficiency, and system adaptability and flexibility. This research proposes a novel movement coordination method for self-governing multiagent systems of intelligent mobile robots. The proposed method uses a leader-follower technique with a virtual leader to maintain a specific geometric structure. Additionally, the epsilon greedy algorithm is utilized to avoid loops. To reduce power consumption, it is proposed to turn on only a few robots' lidars at a time. They could drive all the robots in the group, allowing them to reach the goal without colliding with obstacles. Experiments on a complex map with nine robots were conducted to test the method's effectiveness. The success rate of the swarm reaching the target position and the number of steps needed were evaluated. Testing varied angular velocities of 1 to 20 degrees and linear velocities of 0.1 to 5.5 m/s. Results show the method effectively guides the robots without collisions. This method enables a group of self-governing multiagent systems of intelligent mobile robots to maintain a desired formation while avoiding obstacles and reducing power consumption. The results of the experimental study demonstrate the method's potential to be implemented in real-world missions and traffic management systems to increase efficiency and reduce costs. The proposed method can be utilized in military missions and traffic management systems, where maintaining a specific geometric formation is crucial. The method's ability to avoid obstacles and reduce power consumption can also lead to reduced costs and increased efficiency.

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Service Quality in eLearning

Al-Jawarneh

Mohammed

2022

2022 International Conference on Engineering &Amp; MIS (ICEMIS)

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Formation control and coordination of swarm robotic systems

Cited by 7 publications

References 7 publications

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Development of control software for self-organizing intelligent mobile robots

Service Quality in eLearning

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