Parallel Computational Algorithm for Object-Oriented Modeling of Manipulation Robots

Krakhmalev, Oleg; Korchagin, Sergey; Плешакова, Екатерина; Никитин, П. В.; Tsibizova, Oksana; Sycheva, Irina; Kang, Liang; Serdechnyy, Denis; Gataullin, Sergey; Krakhmalev, Nikita

doi:10.3390/math9222886

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…When dealing with highly redundant robots and those with numerous degrees of freedom, finding the solution to inverse kinematics becomes an even more complex task, as demonstrated by the works presented in [22,23]. A variant of a recurrent SNN, known as LSNN (Long Short-Term Memory spiking neural network), trainable through error gradients, was introduced in [24,25].…”

Section: Snn In Robotic Controlmentioning

confidence: 99%

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Marrero,

Kern,

Urrea

2024

Sensors

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This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

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Section: Snn In Robotic Controlmentioning

confidence: 99%

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Marrero,

Kern,

Urrea

2024

Sensors

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“…All of the above allows us to conclude that one of the promising ways for the development of the agricultural industry is the introduction of robotic solutions, including fast, high-quality, and reliable harvesting [6,7]. At the same time, the key role in such robots should be played by an intelligent image analysis system, which is being developed, in particular, for the tasks of identifying damaged or diseased potatoes [8,9].…”

Section: Introductionmentioning

confidence: 99%

Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415

et al. 2022

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Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.

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“…The issue of safety is key in self-driving cars. Whether self-driving cars will be allowed on the roads depends on an effective solution [9][10][11]. The solution to this problem is multicriteria and comes down to achieving several tasks, one of which is the timely detection and identification of objects and obstacles.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

et al. 2022

Self Cite

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The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.

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Parallel Computational Algorithm for Object-Oriented Modeling of Manipulation Robots

Cited by 8 publications

References 24 publications

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

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