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Robotics
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Mechatronics Systems and Robots
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Mechatronics Systems and Robots

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Intelligent Robots and Mechatronics".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 12575

Special Issue Editor

Dr. Ming Xie

E-Mail Website
Guest Editor
School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
Interests: humanoid robotics (design, control, biped walking, mobile manipulation) and autonomous vehicles (perception, planning, and control)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mechatronics systems and robots are widely being used in various contemporary scenarios, which have a great influence on the real world. Mechatronics involves a synergistic combination of mechanical engineering, electronics, and measurement and control in the design of products and processes; robots, with their versatility and adaptivity, have become the most promising mechatronic system. Thus, there is increasing interest in research on both fields, as well as the fundamental interactions between them.

In this Special Issue, we would like to select outstanding papers from the 8th International Conference on Mechatronics System and Robots (ICMSR 2022), as well as to invite contributions of papers from authors outside the conference participants, in order to disseminate and share the recent progress in the following areas:

  • Mechanism and machine science;
  • Mechanical dynamics and its applications;
  • Dynamic mechanical analysis, optimization, and control;
  • Vibration, noise analysis, and control;
  • Bio-inspired robotics;
  • Force sensors, accelerometers, and other measuring devices;
  • Robots in manufacturing;
  • Kinematics and dynamics analysis;
  • Robotic perceptions and decisions.

Dr. Ming Xie
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. Robotics is an international peer-reviewed open access monthly 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 1800 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.

Published Papers (4 papers)

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Research

16 pages, 9414 KiB  
Article
AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research & Education
by Tanmay Samak, Chinmay Samak, Sivanathan Kandhasamy, Venkat Krovi and Ming Xie
Robotics 2023, 12(3), 77; https://doi.org/10.3390/robotics12030077 - 26 May 2023
Cited by 4 | Viewed by 3149
Abstract
Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and [...] Read more.
Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single- and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path-planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things. Full article
(This article belongs to the Special Issue Mechatronics Systems and Robots)
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18 pages, 13775 KiB  
Article
Design and Scaling of Exoskeleton Power Units Considering Load Cycles of Humans
by Marcel Waldhof, Isabell Wochner, Katrin Stollenmaier, Nejila Parspour and Syn Schmitt
Robotics 2022, 11(5), 107; https://doi.org/10.3390/robotics11050107 - 08 Oct 2022
Viewed by 1699
Abstract
Exoskeletons are powerful tools for aiding humans with pathological conditions, in dangerous environments or in manually exhausting tasks. Typically, they are designed for specific maximum scenarios without taking into account the diversity of tasks and the individuality of the user. To address this [...] Read more.
Exoskeletons are powerful tools for aiding humans with pathological conditions, in dangerous environments or in manually exhausting tasks. Typically, they are designed for specific maximum scenarios without taking into account the diversity of tasks and the individuality of the user. To address this discrepancy, a framework was developed for personalizing an exoskeleton by scaling the components, especially the electrical machine, based on different simulated human muscle forces. The main idea was to scale a numerical arm model based on body mass and height to predict different movements representing both manual labor and daily activities. The predicted torques necessary to produce these movements were then used to generate a load/performance cycle for the power unit design. Considering these torques, main operation points of this load cycle were defined and a reference power unit was scaled and optimized. Therefore, a scalability model for an electrical machine is introduced. This individual adaptation and scaling of the power unit for different users leads to a better performance and a lighter design. Full article
(This article belongs to the Special Issue Mechatronics Systems and Robots)
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23 pages, 4048 KiB  
Article
Online Deflection Compensation of a Flexible Hydraulic Loader Crane Using Neural Networks and Pressure Feedback
by Konrad Johan Jensen, Morten Kjeld Ebbesen and Michael Rygaard Hansen
Robotics 2022, 11(2), 34; https://doi.org/10.3390/robotics11020034 - 17 Mar 2022
Viewed by 3202
Abstract
The deflection compensation of a hydraulically actuated loader crane is presented. Measurement data from the laboratory are used to design a neural network deflection estimator. Kinematic expressions are derived and used with the deflection estimator in a feedforward topology to compensate for the [...] Read more.
The deflection compensation of a hydraulically actuated loader crane is presented. Measurement data from the laboratory are used to design a neural network deflection estimator. Kinematic expressions are derived and used with the deflection estimator in a feedforward topology to compensate for the static deflection. A dynamic deflection compensator is implemented, using pressure feedback and an adaptive bandpass filter. Simulations are conducted to verify the performance of the control system. Experimental results showcase the effectiveness of both the static and dynamic deflection compensator while running closed-loop motion control, with a 90% decrease in static deflection. Full article
(This article belongs to the Special Issue Mechatronics Systems and Robots)
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22 pages, 6035 KiB  
Article
Gait Transition from Pacing by a Quadrupedal Simulated Model and Robot with Phase Modulation by Vestibular Feedback
by Takahiro Fukui, Souichiro Matsukawa, Yasushi Habu and Yasuhiro Fukuoka
Robotics 2022, 11(1), 3; https://doi.org/10.3390/robotics11010003 - 25 Dec 2021
Cited by 4 | Viewed by 3167
Abstract
We propose a method to achieve autonomous gait transition according to speed for a quadruped robot pacing at medium speeds. We verified its effectiveness through experiments with the simulation model and the robot we developed. In our proposed method, a central pattern generator [...] Read more.
We propose a method to achieve autonomous gait transition according to speed for a quadruped robot pacing at medium speeds. We verified its effectiveness through experiments with the simulation model and the robot we developed. In our proposed method, a central pattern generator (CPG) is applied to each leg. Each leg is controlled by a PD controller based on output from the CPG. The four CPGs are coupled, and a hard-wired CPG network generates a pace pattern by default. In addition, we feed the body tilt back to the CPGs in order to adapt to the body oscillation that changes according to the speed. As a result, our model and robot achieve stable changes in speed while autonomously generating a walk at low speeds and a rotary gallop at high speeds, despite the fact that the walk and rotary gallop are not preprogramed. The body tilt angle feedback is the only factor involved in the autonomous generation of gaits, so it can be easily used for various quadruped robots. Therefore, it is expected that the proposed method will be an effective control method for quadruped robots. Full article
(This article belongs to the Special Issue Mechatronics Systems and Robots)
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