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Applied Sciences
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Applications of AI in Robotic Control Systems
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Applications of AI in Robotic Control Systems

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 September 2022) | Viewed by 8281

Special Issue Editors

Prof. Dr. Kyungtae Kang

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea
Interests: interdisciplinary area of cyber-physical systems; medical AI
Special Issues, Collections and Topics in MDPI journals
Dr. Junggab Son

E-Mail Website
Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: applied cryptography, security and privacy in various critical applications; data science in cybersecurity, and blockchains and smart contracts
Special Issues, Collections and Topics in MDPI journals
Dr. Hyo-Joong Suh

E-Mail Website
Guest Editor
School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14462, Korea
Interests: mobile systems; electronic identification system; wireless systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence is being integrated into robots in order to develop advanced robotics that can perform multiple tasks and learn new things with a better perception of the environment, allowing robots to perform critical tasks with a human-like vision to detect or recognize various objects. Intelligent robots have been developed successfully using AI technologies based on machine learning and deep learning. Robotics performance is improving as higher-quality and precise machine-learning procedures are utilized to train computer vision models so that they can distinguish various things and carry out operations appropriately with the desired result. The many types of datasets used to train AI models designed for robots are welcomed in this Special Issue.

AI in robotics not only aids in the learning of models to perform specific tasks, but also makes machines more intelligent to act in a variety of scenarios. Robots incorporate a variety of functions, such as computer vision, motion control, object grasping, and training data to understand physical and logistical data patterns and act accordingly. Multiple sensors providing sensing technology into changing and uncontrolled environments—from motion, time-of-flight optical, temperature and humidity, ultrasonic, and vibration sensors to computer vision for object detection—are required to make AI technologies work effectively in robotics.

In this Special Issue, original works on the theory and use of AI in robotics control systems are sought from a wide range of interdisciplinary viewpoints. The list of applications includes (but is not limited to): improving a robot's visual acuity and image recognition accuracy; developing the best, most efficient ways to use its moving parts (grasping and manipulation); as well as exploring its surroundings, learning more about where it is, what obstacles it will have to navigate, and what challenges it will have to overcome in order to accomplish the tasks important to its primary purpose. For practical case studies, various robotics can be used in healthcare, surgery, agriculture, automotive, warehouses, and supply chain management.

Prof. Dr. Kyungtae Kang
Dr. Junggab Son
Dr. Hyo-Joong Suh
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

  • robotic control
  • machine learning
  • deep learning
  • cyber-physical systems
  • intelligent computing

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Published Papers (2 papers)

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Research

24 pages, 10675 KiB  
Article
Generating Digital Twins for Path-Planning of Autonomous Robots and Drones Using Constrained Homotopic Shrinking for 2D and 3D Environment Modeling
by Martin Denk, Sebastian Bickel, Patrick Steck, Stefan Götz, Harald Völkl and Sandro Wartzack
Appl. Sci. 2023, 13(1), 105; https://doi.org/10.3390/app13010105 - 22 Dec 2022
Cited by 8 | Viewed by 4129
Abstract
A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging [...] Read more.
A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging due to the large number of different dynamic environments and multi-task and agent systems. However, common path algorithms are often limited to two tasks and to finding shortest paths. In real applications, not only a short path but also the width of the passage with a path as centered as possible are crucial, since robotic systems are not ideal and require recalibration frequently. In this work, so-called homotopic shrinking is used to generate the digital twin, which can be used to extract all possible path proposals including their passage widths for 2D and 3D environments and multiple tasks and robots. The erosion of the environment is controlled by constraints such that the task stations, the robot or drone positions, and the topology of the environment are considered. Such a deterministic path algorithm can flexibly respond to changing environmental conditions and consider multiple tasks simultaneously for path generation. A distinctive feature of these paths is the central orientation to the non-passable areas, which can have significant benefits for worker and patient safety. The method is tested on 2D and 3D maps with different tasks, obstacles, and multiple robots. For example, the robust generation of the digital twin for a maze and also the dynamic adaptation in case of sudden changes in the environment is covered. This variety of use cases and the comparison with alternative methods result in significant advantages, such as high robustness, consideration of multiple targets, and high safety distances to obstacles and areas that cannot be traversed. Finally, it was shown that the environment for the digital twin can be reduced to reasonable paths by constrained shrinking, both for real 2D maps and for complex virtual 2D and 3D maps. Full article
(This article belongs to the Special Issue Applications of AI in Robotic Control Systems)
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19 pages, 7273 KiB  
Article
Research on Door Opening Operation of Mobile Robotic Arm Based on Reinforcement Learning
by Yang Wang, Liming Wang and Yonghui Zhao
Appl. Sci. 2022, 12(10), 5204; https://doi.org/10.3390/app12105204 - 20 May 2022
Cited by 12 | Viewed by 3174
Abstract
The traditional robotic arm control method has strong dependence on the application scenario. To improve the reliability of the mobile robotic arm control when the scene is disturbed, this paper proposes a control method based on an improved proximal policy optimization algorithm. This [...] Read more.
The traditional robotic arm control method has strong dependence on the application scenario. To improve the reliability of the mobile robotic arm control when the scene is disturbed, this paper proposes a control method based on an improved proximal policy optimization algorithm. This study researches mobile robotic arms for opening doors. At first, the door handle position is obtained through an image-recognition method based on YOLOv5. Second, the simulation platform CoppeliaSim is used to realize the interaction between the robotic arm and the environment. Third, a control strategy based on a reward function is designed to train the robotic arm and applied to the opening-door task in the real environment. The experimental results show that the proposed method can accelerate the convergence of the training process. Besides, our method can effectively reduce the jitter of the robotic arm and improve the stability of control. Full article
(This article belongs to the Special Issue Applications of AI in Robotic Control Systems)
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