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Electronics
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Neural Networks in Robot-Related Applications
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Neural Networks in Robot-Related Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 10248

Special Issue Editors

Dr. Josip Musić

E-Mail Website
Guest Editor
Department of Electronics and Computer Science, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21 000 Split, Croatia
Interests: mobile robotics; AI in robotics; data fusion; human-machine interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George A. Papakostas

E-Mail Website
Guest Editor
MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue on the usage of neural networks in robotics (with special emphasis on mobile robots) and the associated various research fields such as image processing, automatic control, soft sensor design, etc. As robots (being mobile or not) are coming out of factories and well-structured environments, a certain level of AI has to be embedded into them to ensure their safe and desired behavior. This can be achieved with improved interpretation of sensor data, accessing data that are not readily available with onboard sensors, or with better planning of future control actions to achieve the final goal. Various neural network architecture approaches have recently demonstrated great potential and promise in such and similar scenarios. Thus, this Special Issue addresses all types of such neural networks related to robot applications.

Topics of interest may include but are not limited to original contributions for the following:

  • Neural-network-based approaches for control of mobile and static robots;
  • Neural networks for design of soft sensors in robotics;
  • Neural-network-based interpretation of sensor data;
  • Neural-network-based improvement of sensor performance;
  • Quantization of neural networks in robotics;
  • Innovative applications of neural networks in robotics (including but not limited to ground-based mobile robots, drones, and robotic manipulators).

Prof. Dr. Josip Musić
Prof. Dr. George A. Papakostas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural networks
  • mobile robotics
  • robotic manipulators
  • neural network quantization
  • neural network implementation

Published Papers (5 papers)

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Research

15 pages, 3622 KiB  
Article
Development of Apple Detection System and Reinforcement Learning for Apple Manipulator
by Nikita Andriyanov
Electronics 2023, 12(3), 727; https://doi.org/10.3390/electronics12030727 - 01 Feb 2023
Cited by 7 | Viewed by 1884
Abstract
Modern deep learning systems make it possible to develop increasingly intelligent solutions in various fields of science and technology. The electronics of single board computers facilitate the control of various robotic solutions. At the same time, the implementation of such tasks does not [...] Read more.
Modern deep learning systems make it possible to develop increasingly intelligent solutions in various fields of science and technology. The electronics of single board computers facilitate the control of various robotic solutions. At the same time, the implementation of such tasks does not require a large amount of resources. However, deep learning models still require a high level of computing power. Thus, the effective control of an intelligent robot manipulator is possible when a computationally complex deep learning model on GPU graphics devices and a mechanics control unit on a single-board computer work together. In this regard, the study is devoted to the development of a computer vision model for estimation of the coordinates of objects of interest, as well as the subsequent recalculation of coordinates relative to the control of the manipulator to form a control action. In addition, in the simulation environment, a reinforcement learning model was developed to determine the optimal path for picking apples from 2D images. The detection efficiency on the test images was 92%, and in the laboratory it was possible to achieve 100% detection of apples. In addition, an algorithm has been trained that provides adequate guidance to apples located at a distance of 1 m along the Z axis. Thus, the original neural network used to recognize apples was trained using a big image dataset, algorithms for estimating the coordinates of apples were developed and investigated, and the use of reinforcement learning was suggested to optimize the picking policy. Full article
(This article belongs to the Special Issue Neural Networks in Robot-Related Applications)
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9 pages, 2084 KiB  
Article
Efficient System Identification of a Two-Wheeled Robot (TWR) Using Feed-Forward Neural Networks
by Muhammad Aseer Khan, Dur-e-Zehra Baig, Husan Ali, Bilal Ashraf, Shahbaz Khan, Abdul Wadood and Tariq Kamal
Electronics 2022, 11(21), 3584; https://doi.org/10.3390/electronics11213584 - 02 Nov 2022
Cited by 2 | Viewed by 1460
Abstract
System identification of a Two-Wheeled Robot (TWR) through nonlinear dynamics is carried out in this paper using a data-driven approach. An Artificial Neural Network (ANN) is used as a kinematic estimator for predicting the TWR’s degree of movement in the directions of x [...] Read more.
System identification of a Two-Wheeled Robot (TWR) through nonlinear dynamics is carried out in this paper using a data-driven approach. An Artificial Neural Network (ANN) is used as a kinematic estimator for predicting the TWR’s degree of movement in the directions of x and y and the angle of rotation Ψ along the z-axis by giving a set of input vectors in terms of linear velocity ‘V’ (i.e., generated through the angular velocity ‘ω’ of a DC motor). The DC motor rotates the TWR’s wheels that have a wheel radius of ‘r’. Training datasets are achieved via simulating nonlinear kinematics of the TWR in a MATLAB Simulink environment by varying the linear scale sets of ‘V’ and ‘(r ± ∆r)’. Perturbation of the TWR’s wheel radius at ∆r = 10% is introduced to cater to the robustness of the TWR wheel kinematics. A trained ANN accurately modeled the kinematics of the TWR. The performance indicators are regression analysis and mean square value, whose achieved values met the targeted values of 1 and 0.01, respectively. Full article
(This article belongs to the Special Issue Neural Networks in Robot-Related Applications)
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21 pages, 3133 KiB  
Article
Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning
by Eleni Vrochidou, George K. Sidiropoulos, Athanasios G. Ouzounis, Anastasia Lampoglou, Ioannis Tsimperidis, George A. Papakostas, Ilias T. Sarafis, Vassilis Kalpakis and Andreas Stamkos
Electronics 2022, 11(20), 3289; https://doi.org/10.3390/electronics11203289 - 12 Oct 2022
Cited by 6 | Viewed by 1877
Abstract
Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or [...] Read more.
Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segmentation models with red–green–blue (RGB) marble slab images using five-fold cross-validation, providing consistent evaluation metrics in terms of Intersection over Union (IoU), precision, recall and F1 score to identify the segmentation challenges related to marble cracks’ physiology. Comparative results reveal the FPN model as the most efficient architecture, scoring 71.35% mean IoU, and SE-ResNet as the most effective feature extraction network family. The results indicate the importance of selecting the appropriate Loss function and backbone network, underline the challenges related to the marble crack segmentation problem, and pose an important step towards the robotic automation of crack segmentation and simultaneous resin application to heal cracks in marble-processing plants. Full article
(This article belongs to the Special Issue Neural Networks in Robot-Related Applications)
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13 pages, 3919 KiB  
Article
Person Localization Model Based on a Fusion of Acoustic and Visual Inputs
by Leon Koren, Tomislav Stipancic, Andrija Ricko and Luka Orsag
Electronics 2022, 11(3), 440; https://doi.org/10.3390/electronics11030440 - 01 Feb 2022
Cited by 3 | Viewed by 1571
Abstract
PLEA is an interactive, biomimetic robotic head with non-verbal communication capabilities. PLEA reasoning is based on a multimodal approach combining video and audio inputs to determine the current emotional state of a person. PLEA expresses emotions using facial expressions generated in real-time, which [...] Read more.
PLEA is an interactive, biomimetic robotic head with non-verbal communication capabilities. PLEA reasoning is based on a multimodal approach combining video and audio inputs to determine the current emotional state of a person. PLEA expresses emotions using facial expressions generated in real-time, which are projected onto a 3D face surface. In this paper, a more sophisticated computation mechanism is developed and evaluated. The model for audio-visual person separation can locate a talking person in a crowded place by combining input from the ResNet network with input from a hand-crafted algorithm. The first input is used to find human faces in the room, and the second input is used to determine the direction of the sound and to focus attention on a single person. After an information fusion procedure is performed, the face of the person speaking is matched with the corresponding sound direction. As a result of this procedure, the robot could start an interaction with the person based on non-verbal signals. The model was tested and evaluated under laboratory conditions by interaction with users. The results suggest that the methodology can be used efficiently to focus a robot’s attention on a localized person. Full article
(This article belongs to the Special Issue Neural Networks in Robot-Related Applications)
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16 pages, 5545 KiB  
Article
A Deep Reinforcement Learning Strategy Combining Expert Experience Guidance for a Fruit-Picking Manipulator
by Yuqi Liu, Po Gao, Change Zheng, Lijing Tian and Ye Tian
Electronics 2022, 11(3), 311; https://doi.org/10.3390/electronics11030311 - 19 Jan 2022
Cited by 14 | Viewed by 2160
Abstract
When using deep reinforcement learning algorithms for path planning of a multi-DOF fruit-picking manipulator in unstructured environments, it is much too difficult for the multi-DOF manipulator to obtain high-value samples at the beginning of training, resulting in low learning and convergence efficiency. Aiming [...] Read more.
When using deep reinforcement learning algorithms for path planning of a multi-DOF fruit-picking manipulator in unstructured environments, it is much too difficult for the multi-DOF manipulator to obtain high-value samples at the beginning of training, resulting in low learning and convergence efficiency. Aiming to reduce the inefficient exploration in unstructured environments, a reinforcement learning strategy combining expert experience guidance was first proposed in this paper. The ratios of expert experience to newly generated samples and the frequency of return visits to expert experience were studied by the simulation experiments. Some conclusions were that the ratio of expert experience, which declined from 0.45 to 0.35, was more effective in improving learning efficiency of the model than the constant ratio. Compared to an expert experience ratio of 0.35, the success rate increased by 1.26%, and compared to an expert experience ratio of 0.45, the success rate increased by 20.37%. The highest success rate was achieved when the frequency of return visits was 15 in 50 episodes, an improvement of 31.77%. The results showed that the proposed method can effectively improve the model performance and enhance the learning efficiency at the beginning of training in unstructured environments. This training method has implications for the training process of reinforcement learning in other domains. Full article
(This article belongs to the Special Issue Neural Networks in Robot-Related Applications)
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