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Applied Sciences
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Robotics and Industrial Automation: From Methods to Applications
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Robotics and Industrial Automation: From Methods to Applications

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 5860

Special Issue Editor

Prof. Dr. Mohd Rizal Arshad

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Pulau Pinang, Malaysia
Interests: control and automation; autonomous robotics; sensors and intelligent methods

Special Issue Information

Dear Colleagues,

The Special Issue will focus on the latest advancements and developments in the field of robotics and industrial automation. From methods to applications, this Special Issue will cover a wide range of topics that are shaping the future of this rapidly evolving field. It will explore the various methods used in robotics and industrial automation, such as robotic arms and grippers, sensors, and feedback systems. These technologies are crucial for precise and efficient task execution, and the papers will delve into the latest research in these areas to understand how they are being used to improve the capabilities of robots. This Special Issue will also delve into the various applications of robotics and industrial automation, such as in manufacturing, healthcare, and agriculture. For example, we will discuss how robots are being used in manufacturing for tasks such as welding, painting, and assembly. In healthcare, we will explore how robots are being used for tasks such as surgery and rehabilitation. Additionally, in agriculture, we will discuss how robots are being used for tasks such as planting, harvesting, and monitoring crop growth. Overall, this Special Issue will provide a comprehensive overview of the latest research and developments in the field of robotics and industrial automation, highlighting the most important and exciting advancements in the field, especially in the implementation of intelligent methods such as artificial intelligence into the system. It is hoped that this Special Issue will be of interest to researchers, practitioners, and anyone with a passion for the future of robotics and industrial automation.

Prof. Dr. Mohd Rizal Arshad
Guest Editor

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

  • robotics
  • industrial automation
  • intelligent methods
  • artificial intelligence in manufacturing

Published Papers (5 papers)

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Research

19 pages, 6449 KiB  
Article
Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability
by Dongjun Lee and Sangrok Jin
Appl. Sci. 2024, 14(8), 3384; https://doi.org/10.3390/app14083384 - 17 Apr 2024
Viewed by 421
Abstract
This paper presents a study on optimal design to determine the installation position and link lengths of a robot within a designated workspace for welding, aiming to minimize singularities during the robot’s motion. Bi-objective functions are formulated to minimize singularities while maximizing the [...] Read more.
This paper presents a study on optimal design to determine the installation position and link lengths of a robot within a designated workspace for welding, aiming to minimize singularities during the robot’s motion. Bi-objective functions are formulated to minimize singularities while maximizing the volumes of linear velocity manipulability ellipsoid and angular velocity manipulability ellipsoid, respectively, ensuring isotropy. We have constructed a simulation environment incorporating PID control to account for robot tracking errors. This environment was utilized as a simulator to derive a Bi-objective function set within a genetic algorithm. Through this, we optimized four robot link length variables and two installation position variables, selecting the optimal design variables on the Pareto Front. In the standard work object, the volume average of the linear velocity manipulability ellipsoid was confirmed to have improved by 72% compared to the initial level, and the isotropy of the angular velocity manipulability ellipsoid was confirmed to have improved by 23% compared to the initial level. Furthermore, correlation analysis between design parameters identified those with a high correlation with the objective functions, and the analysis results are discussed. Full article
(This article belongs to the Special Issue Robotics and Industrial Automation: From Methods to Applications)
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21 pages, 2167 KiB  
Article
PID Control Assessment Using L-Moment Ratio Diagrams
by Paweł D. Domański, Krzysztof Dziuba and Radosław Góra
Appl. Sci. 2024, 14(8), 3331; https://doi.org/10.3390/app14083331 - 15 Apr 2024
Viewed by 432
Abstract
This paper presents an application of L-moments and respective L-moment ratio diagrams (LMRD) to the task of control performance assessment (CPA). An L-moment ratio diagram is a graphical approach to the visualization of statistical properties for a given time series. Moreover, it enables [...] Read more.
This paper presents an application of L-moments and respective L-moment ratio diagrams (LMRD) to the task of control performance assessment (CPA). An L-moment ratio diagram is a graphical approach to the visualization of statistical properties for a given time series. Moreover, it enables comparing various data, showing their similarities and homogeneity. Simultaneously, CPA aims at measuring the control loop quality, supporting decision-making about their tuning and maintenance. This paper shows that control system quality can be efficiently visualized using LMRDs. The method was analyzed using simulations and further validated at a real chemical engineering industrial ammonia synthesis plant. Full article
(This article belongs to the Special Issue Robotics and Industrial Automation: From Methods to Applications)
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22 pages, 3752 KiB  
Article
A Model-Free-Based Control Method for Robot Manipulators: Achieving Prescribed Performance and Ensuring Fixed Time Stability
by Anh Tuan Vo, Thanh Nguyen Truong and Hee-Jun Kang
Appl. Sci. 2023, 13(15), 8939; https://doi.org/10.3390/app13158939 - 3 Aug 2023
Cited by 1 | Viewed by 1192
Abstract
This paper addresses three significant challenges in controlling robot manipulators: improving response time, minimizing steady-state errors and chattering, and enhancing controller robustness. It also focuses on eliminating the need for computing the robot’s dynamic model and unknown functions, as well as achieving global [...] Read more.
This paper addresses three significant challenges in controlling robot manipulators: improving response time, minimizing steady-state errors and chattering, and enhancing controller robustness. It also focuses on eliminating the need for computing the robot’s dynamic model and unknown functions, as well as achieving global fixed-time convergence and the prescribed performance for the control system. To achieve these objectives, a fixed-time sliding mode function is designed, which uses transformation errors to achieve prescribed control performance, with adjustments made to the maximum overshoot, convergence time, and tracking errors to keep them within predefined bounds. Additionally, a radial basis function neural network (RBFNN) is used to eliminate the need for knowledge of the robot’s dynamical properties and uncertain terms, which also reduces negative chattering. Finally, a novel fixed-time terminal sliding mode control (TSMC) algorithm is developed for robot manipulators without using their dynamical model. The fixed-time stability of the control system is thoroughly demonstrated by applying Lyapunov criteria and conducting simulations on a robot manipulator to showcase its effectiveness. Full article
(This article belongs to the Special Issue Robotics and Industrial Automation: From Methods to Applications)
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18 pages, 6907 KiB  
Article
Smooth Interpolation Design with Consideration of Corner Tolerance Constraints for Robotics
by Hung-Ming Li, Meng-Shiun Tsai, Ting-Hua Zhang and Chih-Chun Cheng
Appl. Sci. 2023, 13(15), 8789; https://doi.org/10.3390/app13158789 - 29 Jul 2023
Viewed by 1041
Abstract
This paper presents a novel method for interpolation design that ensures the continuity of a velocity profile and satisfies a specified corner tolerance constraint. The method uses an S-shaped profile to generate trajectories for each line segment in the task space. The velocity [...] Read more.
This paper presents a novel method for interpolation design that ensures the continuity of a velocity profile and satisfies a specified corner tolerance constraint. The method uses an S-shaped profile to generate trajectories for each line segment in the task space. The velocity profiles of each segment are overlapped to control the smoothness of the corners and reduce the cycle time. This study defined an overlapping time parameter that is associated with the corner tolerance and the cycle time. Moreover, a corner tolerance constraint equation was derived that can allow for a given tolerance to be satisfied. This constraint equation enables the use of the proposed velocity profile overlap (VPO) method to specify corner tolerances for each corner of the trajectory. The proposed method was compared against the conventional acceleration/deceleration after interpolation (ADAI) method. The results demonstrate that the proposed VPO method can achieve higher accuracy and lower cycle time than the ADAI method. Full article
(This article belongs to the Special Issue Robotics and Industrial Automation: From Methods to Applications)
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11 pages, 2973 KiB  
Article
StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
by Chao Duan, Steffen Junginger, Kerstin Thurow and Hui Liu
Appl. Sci. 2023, 13(10), 5842; https://doi.org/10.3390/app13105842 - 9 May 2023
Viewed by 2200
Abstract
We present a novel stereo visual odometry (VO) model that utilizes both optical flow and depth information. While some existing monocular VO methods demonstrate superior performance, they require extra frames or information to initialize the model in order to obtain absolute scale, and [...] Read more.
We present a novel stereo visual odometry (VO) model that utilizes both optical flow and depth information. While some existing monocular VO methods demonstrate superior performance, they require extra frames or information to initialize the model in order to obtain absolute scale, and they do not take into account moving objects. To address these issues, we have combined optical flow and depth information to estimate ego-motion and proposed a framework for stereo VO using deep neural networks. The model simultaneously generates optical flow and depth information outputs from sequential stereo RGB image pairs, which are then fed into the pose estimation network to achieve final motion estimation. Our experiments have demonstrated that our combination of optical flow and depth information improves the accuracy of camera pose estimation. Our method outperforms existing learning-based and monocular geometry-based methods on the KITTI odometry dataset. Furthermore, we have achieved real-time performance, making our method both effective and efficient. Full article
(This article belongs to the Special Issue Robotics and Industrial Automation: From Methods to Applications)
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