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Advances in Mechatronics Systems and Robotics: Sensing and Control
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Advances in Mechatronics Systems and Robotics: Sensing and Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13488

Special Issue Editor

Prof. Dr. Chin-Sheng Chen

E-Mail Website
Guest Editor
Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: robotics; system modeling and control; mechatronics; machine vision

Special Issue Information

Dear Colleagues,

Due to the development of advanced sensing and control technologies, mechatronic systems and robotics have become intelligent and human-like, allowing for forms of interaction between users and machines, and even between machines.

We encourage the submission of technically rigorous research papers that present recent fundamental new technologies, methods, and applications across all areas of mechatronics systems and robotics.

This Special Issue focuses on recent advances in mechatronics systems and robotics and encourages the researcher to submit their outstanding work toward addressing this issue, focusing on, but not limited to, the following topics:

  • Modeling,identification, and control of mechatronics systems and robotics;
  • Sensing technologies and applications in mechatronics systems and robotics;
  • Fault diagnosis and fault tolerance of mechatronics systems and robotics;
  • Applications of machine vision, pattern recognition, and synthesis;
  • Applications of artificial intelligence, machine learning, and the Internet of Things;
  • Computer-based intelligence of mechatronics systems and robotics;
  • Networked mechatronic systems and robotics;
  • Advances in computational methods for sensing and control;
  • Human-machine teaching and collaborative technology.

Prof. Dr. Chin-Sheng Chen
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. Sensors 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 2600 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.

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Published Papers (4 papers)

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Research

22 pages, 1636 KiB  
Article
Precise and Efficient Pointing Control of a 2.5-m-Wide Field Survey Telescope Using ADRC and Nonlinear Disturbance Observer
by Yang Liu, Yongting Deng, Hongwen Li, Jianli Wang and Dejun Wang
Sensors 2023, 23(13), 6068; https://doi.org/10.3390/s23136068 - 30 Jun 2023
Cited by 1 | Viewed by 1180
Abstract
Linear active disturbance rejection control (LADRC) has been widely used to improve the tracking accuracy and anti-disturbance performance of telescope servo control under disturbances. However, the linear extended state observer (LESO) is sensitive to noise, and its bandwidth is limited by the resonant [...] Read more.
Linear active disturbance rejection control (LADRC) has been widely used to improve the tracking accuracy and anti-disturbance performance of telescope servo control under disturbances. However, the linear extended state observer (LESO) is sensitive to noise, and its bandwidth is limited by the resonant frequency of the telescope. To enhance the LARDC’s ability to attenuate disturbances, a novel cascade anti-disturbance structure (NCADS) with LADRC on the outer speed loop and a nonlinear disturbance observer (NDOB) on the inner current loop is proposed. The NDOB compensates for the dominant disturbance through feedforwarding the q-axis current reference, and the LESO compensates for the residual disturbance on the outer speed loop. First, the NCADS is introduced in a three-closed-loop control framework of PMSM. Then, the design method of the controller for each loop and the NDOB are presented, the parameter-tuning method based on bandwidth is demonstrated, and the convergence of the NDOB is proved. Furthermore, to improve the searching and tracking efficiency of wide-field survey telescopes, the nonlinear tracking differentiator (NTD) was modified to plan the transition process of the position loop, which only needs to set the maximum speed and acceleration of the telescope. Finally, simulations and experiments were performed on a 2.5-m-wide field survey telescope. The experimental results verify that the proposed NCADS method has a better anti-disturbance performance and higher tracking precision than the conventional method, and the improved NTD method does not need to tune parameters and achieved a fast and smooth transition process of the position loop. Full article
(This article belongs to the Special Issue Advances in Mechatronics Systems and Robotics: Sensing and Control)
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22 pages, 12012 KiB  
Article
Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization
by Chin-Sheng Chen and Nien-Tsu Hu
Sensors 2023, 23(3), 1076; https://doi.org/10.3390/s23031076 - 17 Jan 2023
Cited by 4 | Viewed by 3950
Abstract
This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm [...] Read more.
This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper. Full article
(This article belongs to the Special Issue Advances in Mechatronics Systems and Robotics: Sensing and Control)
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16 pages, 4850 KiB  
Article
Towards Haptic-Based Dual-Arm Manipulation
by Sri Harsha Turlapati and Domenico Campolo
Sensors 2023, 23(1), 376; https://doi.org/10.3390/s23010376 - 29 Dec 2022
Cited by 4 | Viewed by 3352
Abstract
Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand−object pose tracking faces challenges such as occlusions and objects moving out of view during robotic manipulation. In this work, we show [...] Read more.
Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand−object pose tracking faces challenges such as occlusions and objects moving out of view during robotic manipulation. In this work, we show that object kinematics can be inferred from local haptic feedback at the robot−object contact points, combined with robot kinematics information given an initial vision estimate of the object pose. A planar, dual-arm, teleoperated robotic setup was built to manipulate an object with hands shaped like circular discs. The robot hands were built with rubber cladding to allow for rolling contact without slipping. During stable grasping by the dual arm robot, under quasi-static conditions, the surface of the robot hand and object at the contact interface is defined by local geometric constraints. This allows one to define a relation between object orientation and robot hand orientation. With rolling contact, the displacement of the contact point on the object surface and the hand surface must be equal and opposite. This information, coupled with robot kinematics, allows one to compute the displacement of the object from its initial location. The mathematical formulation of the geometric constraints between robot hand and object is detailed. This is followed by the methodology in acquiring data from experiments to compute object kinematics. The sensors used in the experiments, along with calibration procedures, are presented before computing the object kinematics from recorded haptic feedback. Results comparing object kinematics obtained purely from vision and from haptics are presented to validate our method, along with the future ideas for perception via haptic manipulation. Full article
(This article belongs to the Special Issue Advances in Mechatronics Systems and Robotics: Sensing and Control)
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19 pages, 1449 KiB  
Article
A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots
by Jiaoyang Lu, Ting Zou and Xianta Jiang
Sensors 2022, 22(22), 8909; https://doi.org/10.3390/s22228909 - 18 Nov 2022
Cited by 12 | Viewed by 3777
Abstract
Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, [...] Read more.
Inverse kinematics problems (IKP) are ubiquitous in robotics for improved robot control in widespread applications. However, the high non-linearity, complexity, and equation coupling of a general six-axis robotic manipulator pose substantial challenges in solving the IKP precisely and efficiently. To address this issue, we propose a novel approach based on neural network (NN) with numerical error minimization in this paper. Within our framework, the complexity of IKP is first simplified by a strategy called joint space segmentation, with respective training data generated by forward kinematics. Afterwards, a set of multilayer perception networks (MLP) are established to learn from the foregoing data in order to fit the goal function piecewise. To reduce the computational cost of the inference process, a set of classification models is trained to determine the appropriate forgoing MLPs for predictions given a specific input. After the initial solution is sought, being improved with a prediction error minimized, the refined solution is finally achieved. The proposed methodology is validated via simulations on Xarm6—a general 6 degrees of freedom manipulator. Results further verify the feasibility of NN for IKP in general cases, even with a high-precision requirement. The proposed algorithm has showcased enhanced efficiency and accuracy compared to NN-based approaches reported in the literature. Full article
(This article belongs to the Special Issue Advances in Mechatronics Systems and Robotics: Sensing and Control)
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