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Intelligent Control and Robotics
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Intelligent Control and Robotics

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 32157

Special Issue Editors

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
Dr. Minsung Kim

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Dongguk University-Seoul Campus, Seoul 04620, Korea
Interests: intelligent controller design for industrial electronics; highly efficient power conversion circuit design; and renewable energy and energy storage systems
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 sharing 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. Further, 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 recent major achievements in the 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.

Prof. Tae-Yong Kuc
Prof. Minsung Kim
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 (4 papers)

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Research

19 pages, 21714 KiB  
Article
Target Recovery for Robust Deep Learning-Based Person Following in Mobile Robots: Online Trajectory Prediction
by Redhwan Algabri and Mun-Taek Choi
Appl. Sci. 2021, 11(9), 4165; https://doi.org/10.3390/app11094165 - 2 May 2021
Cited by 23 | Viewed by 12513
Abstract
The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the [...] Read more.
The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics)
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26 pages, 8652 KiB  
Article
A Hierarchical Control System for Autonomous Driving towards Urban Challenges
by Nam Dinh Van, Muhammad Sualeh, Dohyeong Kim and Gon-Woo Kim
Appl. Sci. 2020, 10(10), 3543; https://doi.org/10.3390/app10103543 - 20 May 2020
Cited by 36 | Viewed by 7213
Abstract
In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently [...] Read more.
In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. The experimental results show that the proposed framework can operate efficiently in the urban scenario. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics)
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30 pages, 6013 KiB  
Article
Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
by Sung-Hyeon Joo, Sumaira Manzoor, Yuri Goncalves Rocha, Sang-Hyeon Bae, Kwang-Hee Lee, Tae-Yong Kuc and Minsung Kim
Appl. Sci. 2020, 10(9), 3219; https://doi.org/10.3390/app10093219 - 5 May 2020
Cited by 32 | Viewed by 8425
Abstract
Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of [...] Read more.
Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics)
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15 pages, 2953 KiB  
Article
In-flight Wind Field Identification and Prediction of Parafoil Systems
by Haitao Gao, Jin Tao, Matthias Dehmer, Frank Emmert-Streib, Qinglin Sun, Zengqiang Chen, Guangming Xie and Quan Zhou
Appl. Sci. 2020, 10(6), 1958; https://doi.org/10.3390/app10061958 - 12 Mar 2020
Cited by 3 | Viewed by 3064
Abstract
The wind field is an essential factor that affects accurate homing and flare landing of parafoil systems. In order to obtain the ambient wind field during the descent of a parafoil system, a combination method of in-flight wind field identification and prediction is [...] Read more.
The wind field is an essential factor that affects accurate homing and flare landing of parafoil systems. In order to obtain the ambient wind field during the descent of a parafoil system, a combination method of in-flight wind field identification and prediction is proposed. First, a wind identification method only using global position system information is derived based on the flight dynamics of parafoil systems. Then a wind field prediction model is constructed using the atmospheric dynamics, and the low-altitude wind field is predicted based on the identified wind field of high-altitude. Finally, simulations of wind field identification and prediction are conducted. The results demonstrate that the proposed method can identify the wind fields precisely and also predict the wind fields reasonably. This method can potentially be applied in practical parafoil systems to provide wind field information for homing tasks. Full article
(This article belongs to the Special Issue Intelligent Control and Robotics)
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