Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Application of Computer Science in Mobile Robots
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Application of Computer Science in Mobile Robots

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 19090

Special Issue Editors

Dr. Marina Paolanti

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: deep learning; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Pierdicca

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile, Edile e dell’ Architettura, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: geospacial artificial intelligence; photogrammetry; remote sensing; extended reality
Special Issues, Collections and Topics in MDPI journals
Dr. Mónica Ballesta Galdeano

E-Mail Website
Guest Editor
Systems Engineering and Automation Department, Universidad Miguel Hernández de Elche (Alicante), 03202 Elche, Spain
Interests: mobile robots; deep learning; localization; mapping; scene recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in artificial intelligence (AI) techniques have attracted research into how they can be used in robotic systems. This Special Issue seeks to provide readers with an overview and applications of computer science and its related technologies such as machine and deep learning and their potential applications in mobile robots. The Issue is devoted to original research papers on techniques, applications, and industrial case studies of the design and deployment based on formal methods of robotic systems. The focus includes all aspects of modelling, simulation, testing, and implementation for the validation and verification of robotic systems. We seek high quality contributions of articles that advance AI along with its related technologies such as natural language processing, robotics, and machine and deep learning. We also welcome papers about incorporation of these technologies into actual products and services. Visionary papers describing futuristic applications and domain advancements are also encouraged. Potential topics of interest include, but are not limited to, the following:

  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Neural networks
  • Expert systems
  • Pattern recognition
  • Humanoid robots
  • Space and underwater robots
  • Assistive robots
  • Mobile robots
  • Autonomous robots
  • Human–robot interaction
  • Robotic automation
Dr. Marina Paolanti
Dr. Roberto Pierdicca
Dr. Mónica Ballesta Galdeano
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. Applied Sciences 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

  • deep learning
  • machine learning
  • computer science
  • artificial intelligence
  • mobile robots
  • intelligent systems

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 69854 KiB  
Article
Omni-Directional Semi-Global Stereo Matching with Reliable Information Propagation
by Yueyang Ma, Ailing Tian, Penghui Bu, Bingcai Liu and Zixin Zhao
Appl. Sci. 2022, 12(23), 11934; https://doi.org/10.3390/app122311934 - 23 Nov 2022
Cited by 2 | Viewed by 1457
Abstract
High efficiency and accuracy of semi-global matching (SGM) make it widely used in many stereo vision applications. However, SGM not only struggles in dealing with pixels in homogeneous area, but also suffers from streak artifacts. In this paper, we propose a novel omni-directional [...] Read more.
High efficiency and accuracy of semi-global matching (SGM) make it widely used in many stereo vision applications. However, SGM not only struggles in dealing with pixels in homogeneous area, but also suffers from streak artifacts. In this paper, we propose a novel omni-directional SGM (OmniSGM) with a cost volume update scheme to aggregate costs from paths along all directions and to encourage reliable information to propagate across entire image. Specifically, we perform SGM along four tree structures, namely trees in the left, right, top and bottom of root node, and then fuse the outputs to obtain final result. The contributions of pixels on each tree can be recursively computed from leaf nodes to root node, ensuring our method has linear time computational complexity. Moreover, An iterative cost volume update scheme is proposed using aggregated cost in the last pass to enhance the robustness of initial matching cost. Thus, useful information is more likely to propagate in a long distance to handle the ambiguities in low textural area. Finally, we present an efficient strategy to propagate disparities of stable pixels along the minimum spanning tree (MST) for disparity refinement. Extensive experiments in stereo matching on Middlebury and KITTI datasets demonstrate that our method outperforms typical traditional SGM-based cost aggregation methods. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots)
►▼ Show Figures

Figure 1

25 pages, 927 KiB  
Article
A Model-Based Approach for Common Representation and Description of Robotics Software Architectures
by Valery Marcial Monthe, Laurent Nana and Georges Edouard Kouamou
Appl. Sci. 2022, 12(6), 2982; https://doi.org/10.3390/app12062982 - 15 Mar 2022
Cited by 1 | Viewed by 2144
Abstract
Unlike conventional software, robotic software suffers from a lack of methods and processes that could systematize and facilitate development. Thus, the application of software engineering techniques is at the heart of current issues in robotics. The work presented in this paper aims to [...] Read more.
Unlike conventional software, robotic software suffers from a lack of methods and processes that could systematize and facilitate development. Thus, the application of software engineering techniques is at the heart of current issues in robotics. The work presented in this paper aims to facilitate the development of robotic software and to facilitate communication between experts in the field through the use of software engineering techniques and methods. It proposes RsaML (Robotic Software Architecture Modeling Language), a Domain Specific Modeling Language (DSML) dedicated to robotics, which takes into account the different categories of robotic software architectures and makes it possible to describe the latter independently from the implementation platform. The conceptual model defining the terminology and the hierarchy of concepts used for the description and representation of robotic software architectures in RsaML are presented in this article. RsaML is defined through a meta-model which represents the abstract syntax of the language. The real-time properties of robotic software architectures are identified and included in the meta-model. The use of RsaML is illustrated through several experimental scenarios of the language: the definition of a robotic system and the description of its software architecture, the verification of the semantics of a robotic software architecture, and the modeling of a robotic system whose software architecture does not belong to the usual categories. The support tool used for implementations and experimentation is Eclipse Modeling Framework (EMF). The results of experimentation showed good working of the proposed solution and made it possible to validate the main concepts of the RsaML language. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots)
►▼ Show Figures

Figure 1

22 pages, 14882 KiB  
Article
Autonomous Exploration of Mobile Robots via Deep Reinforcement Learning Based on Spatiotemporal Information on Graph
by Zhiwen Zhang, Chenghao Shi, Pengming Zhu, Zhiwen Zeng and Hui Zhang
Appl. Sci. 2021, 11(18), 8299; https://doi.org/10.3390/app11188299 - 7 Sep 2021
Cited by 3 | Viewed by 2469
Abstract
In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical trajectories, frontier waypoints, landmarks, and obstacles. Meanwhile, to take [...] Read more.
In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical trajectories, frontier waypoints, landmarks, and obstacles. Meanwhile, to take full advantage of the spatiotemporal feature and historical information in the autonomous exploration task, we propose a novel network called Spatiotemporal Neural Network on Graph (Graph-STNN). Specifically, the proposed Graph-STNN extracts the spatial feature using graph convolutional network (GCN) and the temporal feature using temporal convolutional network (TCN). Then, gated recurrent unit (GRU) is performed to synthesize the spatial feature, the temporal feature, and the historical state information into the current state feature. Combined with DRL, our Graph-STNN helps estimation of the optimal target point through extracted hybrid features. The simulation experiment shows that our approach is more effective than the GCN-based approach and the information entropy-based approach. Moreover, Graph-STNN also performs better generalization ability than GCN-based, information entropy-based, and random methods. Finally, we validate our approach on the simulation platform Stage with the actual robot model. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots)
►▼ Show Figures

Figure 1

18 pages, 2670 KiB  
Article
A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
by Mónica Ballesta, Luis Payá, Sergio Cebollada, Oscar Reinoso and Francisco Murcia
Appl. Sci. 2021, 11(16), 7521; https://doi.org/10.3390/app11167521 - 16 Aug 2021
Cited by 9 | Viewed by 2805
Abstract
Understanding the environment is an essential ability for robots to be autonomous. In this sense, Convolutional Neural Networks (CNNs) can provide holistic descriptors of a scene. These descriptors have proved to be robust in dynamic environments. The aim of this paper is to [...] Read more.
Understanding the environment is an essential ability for robots to be autonomous. In this sense, Convolutional Neural Networks (CNNs) can provide holistic descriptors of a scene. These descriptors have proved to be robust in dynamic environments. The aim of this paper is to perform hierarchical localization of a mobile robot in an indoor environment by means of a CNN. Omnidirectional images are used as the input of the CNN. Experiments include a classification study in which the CNN is trained so that the robot is able to find out the room where it is located. Additionally, a transfer learning technique transforms the original CNN into a regression CNN which is able to estimate the coordinates of the position of the robot in a specific room. Regarding classification, the room retrieval task is performed with considerable success. As for the regression stage, when it is performed along with an approach based on splitting rooms, it also provides relatively accurate results. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots)
►▼ Show Figures

Figure 1

16 pages, 3991 KiB  
Article
Mobile Robot Path Optimization Technique Based on Reinforcement Learning Algorithm in Warehouse Environment
by HyeokSoo Lee and Jongpil Jeong
Appl. Sci. 2021, 11(3), 1209; https://doi.org/10.3390/app11031209 - 28 Jan 2021
Cited by 33 | Viewed by 5582
Abstract
This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path [...] Read more.
This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment and conditions. Thereafter, we attempted to improve the previous experiment and conducted additional experiments to confirm the improvement. The experimental results helped us understand the characteristics and differences in the reinforcement learning algorithm. The findings of this study will facilitate our understanding of the basic concepts of reinforcement learning for further studies on more complex and realistic path optimization algorithm development. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots)
►▼ Show Figures

Figure 1

17 pages, 4667 KiB  
Article
Study on Comprehensive Calibration and Image Sieving for Coal-Gangue Separation Parallel Robot
by Deyong Shang, Yuwei Wang, Zhiyuan Yang, Junjie Wang and Yue Liu
Appl. Sci. 2020, 10(20), 7059; https://doi.org/10.3390/app10207059 - 11 Oct 2020
Cited by 14 | Viewed by 3111
Abstract
Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts [...] Read more.
Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts by configuring the image recognition system. Robot calibration technology can reduce the influence of installation error on system accuracy and provides the basis for the robot to accurately track and grab gangue. Due to the fact that the angle deflection error between the conveyor belt coordinate system and the robot coordinate system is not considered in the traditional conveyor belt calibration method, an improved comprehensive calibration method is put forward in this paper. Firstly, the working principle and image recognition and positioning process of the Delta coal gangue sorting robot are introduced. The scale factor parameter Factorc of the conveyor encoder is adopted to characterize the relationship between the moving distance of the conveyor and the encoder. The conveyor belt calibration experiment is described in detail. The transformation matrix between the camera, the conveyor belt, and the robot are obtained after establishment of the three respective coordinate systems. The experimental results show that the maximum cumulative deviation of traditional calibration method is 13.841 mm and the comprehensive calibration method is 3.839 mm. The main innovation of the comprehensive calibration is such that the accurate position of each coordinate in the robot coordinate system can be determined. This comprehensive calibration method is simple and feasible, and can effectively improve system calibration accuracy and reduce robot installation error on the grasping accuracy. Moreover, a calculation method to eliminate duplicate images is put forward, with the frame rate of the vision system set at seven frames per second to avoid image repetition acquisition and missing images. The experimental results show that this calculation method effectively improves the processing efficiency of the recognition system, thereby meeting the demands of the grab precision of coal gangue separation engineering. The goal revolving around “safety with few people and safety with none” can therefore be achieved in coal gangue sorting using robots. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop