FumeBot: A Deep Convolutional Neural Network Controlled Robot

Thomas, Ajith J.; Hedley, John

doi:10.3390/robotics8030062

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Takođe, ustanovljeno je da je mreža pogodna za učenje i obavljanje raznovrsnih zadataka na putevima sa i bez obeleženih traka, kao i to da se njenom primenom može zameniti upotreba konvencionalnog PID kontrolera. Istraživanje [42] takođe koristi 2D CNN kao kontroler, ali specijalno obučenu za izbegavanje prepreka na putu. Mreža obučena za izbegavanje prepreka u laboratoriji postiže tačnost od 95% do 97%, dok druga mreža, za izbegavanje prepreka u hodniku ostvaruje tačnost od 93-96%.…”

Section: Dvodimenzionalne Konvolucione Mreže -2dunclassified

Convolutional neural networks in automatic control systems: The state-of-the-art

Perišić,

Jovanović

2023

Tehnika

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Convolutional neural networks are type of deep neural networks used for classification, identification, prediction and object detection. They are sutable for dealing with input data of various dimensions, such as signals, images and videos. Their importance is confirmed by the fact that they are used more than any other type of deep networks. This is the reason for constant development of new algorithms that improve existing models or creation od new models that accelerate or ameliorate learning process. They are utilized in a wide range of scientific and industrial fields due to their possibility of achieving high accuracy and simplicity of implementation. In this paper structure of convolutional networks is presented and, in particular, novelties in the study of convolutional layer are discussed, where different types of convolution are interpreted. Additionaly, special attention has been paid to the use of these networks in control systems in recent years, as a result of the occurrence of Industry 4.0. During scientific work analysis, convolutional networks application are divided according to the dimensionality of input data, that is, according to the dimensionality of networks and the tasks that they can solve.

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Section: Dvodimenzionalne Konvolucione Mreže -2dunclassified

Convolutional neural networks in automatic control systems: The state-of-the-art

Perišić,

Jovanović

2023

Tehnika

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“…More recent efforts to improve the predictive capabilities of autonomous mobile robots have been based upon the development of increasingly sophisticated artificial neural networks (ANNs) [9]. For example, Tai et al [10] implemented an ANN by integrating a convolutional neural network(CNN)with the respective decision-making process to facilitate effective robot exploration based on visual cues. The autonomous mobile robot system was trained using annotated visual information related to the exploration task, and its efficiency was evaluated in a real-time application.…”

Section: Introductionmentioning

confidence: 99%

Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

Murugan¹,

Chinnadurai²,

Manikandan³

2022

Computers, Materials &Amp; Continua

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The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years. However, conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories. The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time. The proposed system is implemented using the AlphaBot platform, and the performance of the system is evaluated according to the obstacle prediction accuracy, path detection accuracy, time-lapse, tour length, and the overall accuracy of the system. The proposed system provide a very high obstacle prediction accuracy of 99.61%. Accordingly, the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.

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“…Implementing experimental results in two different robot control tasks on real root systems has been shown in [4], which is a rare example of a real robot system experiment. A convolutional neural network's development to control a home surveillance robot has been described in [5].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

Pavlic¹,

Kušec²,

Radočaj³

et al. 2021

Applied Sciences

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In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot’s motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot’s motion. Use of the basic cognitive elements language, a robot’s environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot’s environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems.

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FumeBot: A Deep Convolutional Neural Network Controlled Robot

Cited by 12 publications

References 22 publications

Convolutional neural networks in automatic control systems: The state-of-the-art

Convolutional neural networks in automatic control systems: The state-of-the-art

Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

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