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Applied Sciences
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Intelligent Control and Robotics II
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Intelligent Control and Robotics II

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2098

Special Issue Editors

Dr. Minsung Kim

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Guest Editor
Department of Electronic and Electrical Engineering, Dongguk University-Seoul Campus, Seoul 04620, Republic of Korea
Interests: highly efficient power conversion circuit design; intelligent controller design for industrial electronics; renewable energy and energy storage systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tae-Yong Kuc

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-dong Jangan-gu, Suwon 440-746, Gyeonggi-do, Republic of Korea
Interests: autonomous navigation of mobile robots; VSLAM; 3D SLAM; semantic SLAM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent control and robotics are closely related to each other and have been the subject of many years of research. With the growing need of robots that can perform useful tasks for humans, human-like learning and cognitive skills are required for the upcoming intelligent robots that share common surroundings with humans, such as homes, offices, factories, and outdoor environments. In view of this, intelligent control and robotics have been evolving in such a way that the topics accommodate and take advantage of a large spectrum of convergence technologies developed from algorithmic research, symbolic AI, and computational AI using rule-based knowledge modeling, neural networks, fuzzy logic, GAs, and more recently, deep neural networks. Furthermore, since intelligent robots should support target tasks involving high-level planning and control strategies for manipulation, navigation, and interaction (human–robot interaction), a recent trend in robot intelligence research is to combine traditional data-driven approaches with the knowledge-driven approaches motivated by cognitive science and brain research. This extends the coverage of topics for this Special Issue from motor-level learning and trajectory control to semantic SLAM (simultaneous localization and mapping) and scene understanding for intelligent control. We feel that the timing of this Special Issue is favorable, given the recent major achievements in related research, such as the finding of brain GPS function in neuroscience and physiology and the high performance of state-of-the-art deep-learning-based recognition.

We encourage researchers in this field to contribute their original papers to share their technical achievements with the readers of this Special Issue.

Prof. Dr. Minsung Kim
Prof. Dr. Tae-Yong Kuc
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 and neural approaches for robotics
  • adaptive learning control for robotics
  • intelligent control of autonomous robots in dynamic environments
  • automated and intelligent path planning of mobile robots
  • cooperative robots and distributed control
  • semantic SLAM
  • 3D SLAM
  • visual SLAM
  • place recognition and scene understanding
  • fault detection and diagnosis of self-recovery robots

Related Special Issue

Published Papers (2 papers)

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Research

17 pages, 1212 KiB  
Article
A Novel Zeroing Neural Network Control Scheme for Tracked Mobile Robot Based on an Extended State Observer
by Yuxuan Cao and Jinyun Pu
Appl. Sci. 2024, 14(1), 303; https://doi.org/10.3390/app14010303 - 29 Dec 2023
Viewed by 542
Abstract
A novel zeroing neural network control scheme based on an extended state observer is proposed for the trajectory tracking of a tracked mobile robot which is subject to unknown external disturbances and uncertainties. To estimate unknown lumped disturbances and unmeasured velocities, a third-order [...] Read more.
A novel zeroing neural network control scheme based on an extended state observer is proposed for the trajectory tracking of a tracked mobile robot which is subject to unknown external disturbances and uncertainties. To estimate unknown lumped disturbances and unmeasured velocities, a third-order fixed-time extended state observer is proposed, and the observation errors converge to zero in fixed time. Based on the estimated values, the zeroing neural network controller is designed for a tracked mobile robot to track an eight shape. The stability of the system is analyzed based on Lyapunov theory. Simulation results are illustrated to show the effectiveness of the proposed control scheme. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics II)
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17 pages, 16909 KiB  
Article
Adaptive Locomotion Learning for Quadruped Robots by Combining DRL with a Cosine Oscillator Based Rhythm Controller
by Xiaoping Zhang, Yitong Wu, Huijiang Wang, Fumiya Iida and Li Wang
Appl. Sci. 2023, 13(19), 11045; https://doi.org/10.3390/app131911045 - 07 Oct 2023
Viewed by 1284
Abstract
Animals have evolved to adapt to complex and uncertain environments, acquiring locomotion skills for diverse surroundings. To endow a robot’s animal-like locomotion ability, in this paper, we propose a learning algorithm for quadruped robots based on deep reinforcement learning (DRL) and a rhythm [...] Read more.
Animals have evolved to adapt to complex and uncertain environments, acquiring locomotion skills for diverse surroundings. To endow a robot’s animal-like locomotion ability, in this paper, we propose a learning algorithm for quadruped robots based on deep reinforcement learning (DRL) and a rhythm controller that is based on a cosine oscillator. For a quadruped robot, two cosine oscillators are utilized at the hip joint and the knee joint of one leg, respectively, and, finally, eight oscillators form the controller to realize the quadruped robot’s locomotion rhythm during moving. The coupling between the cosine oscillators of the rhythm controller is realized by the phase difference, which is simpler and easier to realize when dealing with the complex coupling relationship between different joints. DRL is used to help learn the controller parameters and, in the reward function design, we address the challenge of terrain adaptation without relying on the complex camera-based vision processing but based on the proprioceptive information, where a state estimator is introduced to achieve the robot’s posture and help finally utilize the food-end coordinate. Experiments are carried out in CoppeliaSim, and all of the flat, uphill and downhill conditions are considered. The results show that the robot can successfully accomplish all the above skills and, at the same time, with the reward function designed, the robot’s pitch angle, yaw angle and roll angle are very small, which means that the robot is relatively stable during walking. Then, the robot is transplanted to a new scene; the results show that although the environment is previously unencountered, the robot can still fulfill the task, which demonstrates the effectiveness and robustness of this proposed method. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics II)
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