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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: 20 February 2025 | Viewed by 7709

Special Issue Editors

Dr. Minsung Kim

E-Mail Website
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

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Related Special Issue

Published Papers (5 papers)

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Research

17 pages, 48887 KiB  
Article
Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
by Yansong Lin, Qiaoyu Xu, Wenhao Ju and Tianle Zhang
Appl. Sci. 2024, 14(13), 5551; https://doi.org/10.3390/app14135551 - 26 Jun 2024
Cited by 1 | Viewed by 1038
Abstract
In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider [...] Read more.
In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm’s end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics II)
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21 pages, 36012 KiB  
Article
DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
by Wei Qian, Jiansheng Peng and Hongyu Zhang
Appl. Sci. 2024, 14(11), 4949; https://doi.org/10.3390/app14114949 - 6 Jun 2024
Viewed by 1784
Abstract
Visual SLAM technology is one of the important technologies for mobile robots. Existing feature-based visual SLAM techniques suffer from tracking and loop closure performance degradation in complex environments. We propose the DFD-SLAM system to ensure outstanding accuracy and robustness across diverse environments. Initially, [...] Read more.
Visual SLAM technology is one of the important technologies for mobile robots. Existing feature-based visual SLAM techniques suffer from tracking and loop closure performance degradation in complex environments. We propose the DFD-SLAM system to ensure outstanding accuracy and robustness across diverse environments. Initially, building on the ORB-SLAM3 system, we replace the original feature extraction component with the HFNet network and introduce a frame rotation estimation method. This method determines the rotation angles between consecutive frames to select superior local descriptors. Furthermore, we utilize CNN-extracted global descriptors to replace the bag-of-words approach. Subsequently, we develop a precise removal strategy, combining semantic information from YOLOv8 to accurately eliminate dynamic feature points. In the TUM-VI dataset, DFD-SLAM shows an improvement over ORB-SLAM3 of 29.24% in the corridor sequences, 40.07% in the magistrale sequences, 28.75% in the room sequences, and 35.26% in the slides sequences. In the TUM-RGBD dataset, DFD-SLAM demonstrates a 91.57% improvement over ORB-SLAM3 in highly dynamic scenarios. This demonstrates the effectiveness of our approach. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics II)
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16 pages, 1675 KiB  
Article
Mobile Robot Path Planning Algorithm Based on NSGA-II
by Sitong Liu, Qichuan Tian and Chaolin Tang
Appl. Sci. 2024, 14(10), 4305; https://doi.org/10.3390/app14104305 - 19 May 2024
Cited by 1 | Viewed by 1248
Abstract
Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm [...] Read more.
Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm’s ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics II)
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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 997
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 - 7 Oct 2023
Cited by 1 | Viewed by 2081
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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