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Electronics
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Autonomous Robots: Theory, Methods and Applications
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Autonomous Robots: Theory, Methods and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 6507

Special Issue Editors

Dr. Chunxu Li

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213000, China
Interests: robotics; machine learning; computer vision; human–robot interaction; smart materials for soft robot
Special Issues, Collections and Topics in MDPI journals
Dr. Ashraf Fahmy

E-Mail Website
Guest Editor
School of Mechanical Engineering, EN311, Pay Campus, Swansea University, Swansea SA18EN, UK
Interests: control systems engineering; electrical drive engineering; industrial engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Hooman Samani

E-Mail Website
Guest Editor
School of Computing, Engineering and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: robotics; artificial intelligence; AI for robotics
Prof. Dr. Gang He

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213000, China
Interests: computer vision; intelligent design and manufacturing; deep learning; robot motion plan

Special Issue Information

Dear Colleagues,

Like humans, autonomous robots are capable of independent decision making and taking appropriate action. They should be able to observe their surroundings, make judgments based on what they see and/or have been taught to identify, and then actuate a movement or manipulation in that environment. In terms of robot mobility, these decision-based behaviors can involve (but are not limited to) simple operations such as starting, halting, and swerving to avoid impediments. Autonomous robots are divided into four main categories based on their characteristics and applications: programmable, non-programmable, adaptive, and intelligent robots. They are used in various industries, including medical, military, and home appliance control systems, etc.

Although the related technologies of robots are basically mature, as autonomous robots become increasingly large scale, high tech, long lasting, and multi-functional, some key features still need to be studied and resolved, e.g., multi-communicating, modeling, and autonomous control. The objective of this Special Issue is to facilitate the understanding of current challenges and needs in this field, and to provide visibility for recent breakthroughs in the above-mentioned technologies. The achievement of this objective will contribute to improving the state of the art and promoting further advances in the area, as well as providing opportunities for new viable applications.

Dr. Chunxu Li
Dr. Ashraf Fahmy
Dr. Hooman Samani
Prof. Dr. Gang He
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

  • vehicles and drones
  • swarms
  • robot sensing and communication
  • mission planning
  • intelligent control
  • positioning and localization
  • communications
  • artificial intelligence integration
  • pattern analysis and recognition
  • decision support and safe operation
  • manipulation and locomotion
  • structure optimization
  • relevant applications

Published Papers (4 papers)

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Research

20 pages, 11248 KiB  
Article
Self-Organized Patchy Target Searching and Collecting with Heterogeneous Swarm Robots Based on Density Interactions
by Yalun Xiang, Xiaokang Lei, Zhongxing Duan, Fangnan Dong and Yanru Gao
Electronics 2023, 12(12), 2588; https://doi.org/10.3390/electronics12122588 - 08 Jun 2023
Viewed by 988
Abstract
The issue of searching and collecting targets with patchy distribution in an unknown environment is a challenging task for multiple or swarm robots because the targets are unevenly dispersed in space, which makes the traditional solutions based on the idea of path planning [...] Read more.
The issue of searching and collecting targets with patchy distribution in an unknown environment is a challenging task for multiple or swarm robots because the targets are unevenly dispersed in space, which makes the traditional solutions based on the idea of path planning and full spatial coverage very inefficient and time consuming. In this paper, by employing a novel framework of spatial-density-field-based interactions, a collective searching and collecting algorithm for heterogeneous swarm robots is proposed to solve the challenging issue in a self-organized manner. In our robotic system, two types of swarm robots, i.e., the searching robots and the collecting robots, are included. To start with, the searching robots conduct an environment exploration by means of formation movement with Levy flights; when the targets are detected by the searching robots, they spontaneously form a ring-shaped envelope to estimate the spatial distribution of targets. Then, a single robot is selected from the group to enter the patch and locates at the patch’s center to act as a guiding beacon. Subsequently, the collecting robots are recruited by the guiding beacon to gather the patch targets; they first form a ring-shaped envelope around the target patch and then push the scattered targets inward by using a spiral shrinking strategy; in this way, all targets eventually are stacked near the center of the target patch. With the cooperation of the searching robots and the collecting robots, our heterogeneous robotic system can operate autonomously as a coordinated group to complete the task of collecting targets in an unknown environment. Numerical simulations and real swarm robot experiments (up to 20 robots are used) show that the proposed algorithm is feasible and effective, and it can be extended to search and collect different types of targets with patchy distribution. Full article
(This article belongs to the Special Issue Autonomous Robots: Theory, Methods and Applications)
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19 pages, 3021 KiB  
Article
Self-Organized Aggregation Behavior Based on Virtual Expectation of Individuals with Wave-Based Communication
by Phan Gia Luan and Nguyen Truong Thinh
Electronics 2023, 12(10), 2220; https://doi.org/10.3390/electronics12102220 - 13 May 2023
Cited by 1 | Viewed by 1149
Abstract
In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the [...] Read more.
In this study, a microscopic model for a swarm of mobile robots is developed to implement self-organized aggregation behavior. The proposed model relies on the concept of subjective expectation, which is defined as the “minimum wished cluster size” of a robot in the swarm. During the whole process, a robot’s expectation constantly changes based on context awareness. This awareness is obtained by employing a low-cost communication system commonly found in swarm robot studies: infrared-based communication. Robots can make their own decisions by comparing their expected and estimated observed cluster sizes, which leads to macroscopic swarm aggregation. However, due to the limitations of local communication and mobility, robots are restricted in their ability to perceive global information, particularly regarding cluster size. Inspired by the slime mold aggregation process, a wave-based communication mechanism is implemented to help robots estimate a cluster size. Moreover, each transmission includes a modulated message that allows robots to explicitly share their knowledge with others. In this way, despite the fact that the robot may not belong to that cluster due to its perception range, it can easily grasp the cluster size when passing the cluster. Once a robot detects a desired cluster, it can approach this cluster with its direction determined by using average origin of wave (AOW) method. The performance of the aggregation algorithm was tested both in simulation and with a real swarm robot. Dispersion metrics and cluster metrics were employed to evaluate the proposed algorithm’s performance. The results show that the proposed microscopic model utilizes collective behavior which aggregates all randomly distributed robots into a single aggregate cluster with a reasonable swarm density and evaluation time. Full article
(This article belongs to the Special Issue Autonomous Robots: Theory, Methods and Applications)
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13 pages, 4352 KiB  
Article
Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning
by Xiaoyu Ma and Lize Gu
Electronics 2023, 12(4), 975; https://doi.org/10.3390/electronics12040975 - 15 Feb 2023
Cited by 2 | Viewed by 1411
Abstract
In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. [...] Read more.
In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected. Full article
(This article belongs to the Special Issue Autonomous Robots: Theory, Methods and Applications)
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22 pages, 9346 KiB  
Article
Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target
by Gang He, Jianyun Zhou, Hu Yang, Yuan Ning and Huatao Zou
Electronics 2022, 11(19), 3189; https://doi.org/10.3390/electronics11193189 - 05 Oct 2022
Cited by 6 | Viewed by 1970
Abstract
For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the [...] Read more.
For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision [email protected] of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection. Full article
(This article belongs to the Special Issue Autonomous Robots: Theory, Methods and Applications)
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