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Actuators
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Advanced Robots: Design, Control and Application
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Advanced Robots: Design, Control and Application

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Robotics".

Deadline for manuscript submissions: closed (1 August 2022) | Viewed by 21584

Special Issue Editors

Prof. Dr. Ioan Doroftei

E-Mail Website
Guest Editor
Head of Mechanical Engineering, Mechatronics and Robotics Department, “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania
Interests: robotic applications of shape memory alloys; modeling and simulation; mechanisms and machine theory; mechanical engineering; mobile robots, social robotics; rehabilitation robots
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Karsten Berns

E-Mail Website
Guest Editor
Head of Robotics Research Lab, Computer Science Department Technical University of Kaiserslautern, D-67653 Kaiserslautern, Germany
Interests: robot control architectures; autonomous driving and working; biologically motivated robots; mobile robots; humanoid robots; two-legged running; human-robot interaction

Special Issue Information

Dear Colleagues,

Research into the design, control and application of advanced robots has increased during the last few decades, with many different and interesting projects being developed. Advanced robots have many promising applications in various areas of modern society. These robots could yield significant positive impacts on society, but they also carry the potential to cause negative impacts. Therefore, these impacts should be considered and discussed from the perspectives of not only technical solutions but also relevant social issues including safety, law, ethics, psychology and philosophy.

Contributions from all fields related to advanced robots are welcome in this Special Issue, particularly the following:

  • Human–robot interactions (HRI) and social robotics;
  • Safety issues for advanced robots and autonomous systems;
  • Legal and ethical issues for advanced robots;
  • Advanced industrial robots for future manufacturing;
  • Healthcare and medical applications;
  • Service and assistance;
  • Entertainment and education;
  • Robotics and autonomous driving;
  • Artificial intelligence (AI) and robotics;
  • Bio-inspired robotics.

Prof. Dr. Ioan Doroftei
Prof. Dr. Karsten Berns
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. Actuators 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 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

  • human–robot interactions (HRI)
  • safety issues
  • legal and ethical issues
  • advanced industrial robots
  • healthcare
  • service
  • education
  • autonomous driving
  • artificial intelligence (AI)
  • bio-inspired robotics

Published Papers (7 papers)

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Research

13 pages, 4132 KiB  
Article
Control Design and Testing for a Finger Exoskeleton Mechanism
by Adithya Prakash Damarla, Matteo Russo and Marco Ceccarelli
Actuators 2022, 11(8), 230; https://0-doi-org.brum.beds.ac.uk/10.3390/act11080230 - 10 Aug 2022
Cited by 1 | Viewed by 2415
Abstract
This paper describes a control strategy for a linkage finger exoskeleton mechanism with two degrees of freedom. To characterise the performance of the proposed finger motion assistance device, a replica of a human finger is prototyped to mimic human finger motion and to [...] Read more.
This paper describes a control strategy for a linkage finger exoskeleton mechanism with two degrees of freedom. To characterise the performance of the proposed finger motion assistance device, a replica of a human finger is prototyped to mimic human finger motion and to the testing effect of assistance provided by the novel exoskeleton with results from grasp tests. A feasible control design is developed to achieve a robust grasp of an object using the proposed exoskeleton mechanism, which is validated with simulated and experimental results that show how the proposed control algorithm maintains the force within 3% of the desired value. The aim of the paper is to present a control design for the ExoFinger exoskeleton with low-cost easy operation features that are aligned with the similar characteristics of the mechanical design. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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19 pages, 50723 KiB  
Article
SMARCOS: Off-the-Shelf Smart Compliant Actuators for Human–Robot Applications
by Vincent Ducastel, Kevin Langlois, Marco Rossini, Victor Grosu, Bram Vanderborght, Dirk Lefeber, Tom Verstraten and Joost Geeroms
Actuators 2021, 10(11), 289; https://0-doi-org.brum.beds.ac.uk/10.3390/act10110289 - 27 Oct 2021
Cited by 4 | Viewed by 3601
Abstract
With the growing popularity of Human–Robot Interactions, a series of robotic assistive devices have been created over the last decades. However, due to the lack of easily integrable resources, the development of these custom made devices turns out to be long and expensive. [...] Read more.
With the growing popularity of Human–Robot Interactions, a series of robotic assistive devices have been created over the last decades. However, due to the lack of easily integrable resources, the development of these custom made devices turns out to be long and expensive. Therefore, the SMARCOS, a novel off-the-shelf Smart Variable Stiffness Actuator for human-centered robotic applications is proposed in this paper. This modular actuator combines compliant elements and sensors as well as low-level controller and high-bandwidth communication. The characterisation of the actuator is presented in this manuscript, followed by two use-cases wherein the benefits of such technology can be truly exploited. The actuator provides a lightweight design that can serve as the building blocks to facilitate the development of robotic applications. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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20 pages, 5981 KiB  
Article
Development of a Control System and Functional Validation of a Parallel Robot for Lower Limb Rehabilitation
by Doina Pisla, Iuliu Nadas, Paul Tucan, Stefan Albert, Giuseppe Carbone, Tiberiu Antal, Alexandru Banica and Bogdan Gherman
Actuators 2021, 10(10), 277; https://0-doi-org.brum.beds.ac.uk/10.3390/act10100277 - 18 Oct 2021
Cited by 12 | Viewed by 3316
Abstract
This paper is focused on the development of a control system, implemented on a parallel robot designed for the lower limb rehabilitation of bedridden stroke survivors. The paper presents the RECOVER robotic system kinematics, further implemented into the control system, which is described [...] Read more.
This paper is focused on the development of a control system, implemented on a parallel robot designed for the lower limb rehabilitation of bedridden stroke survivors. The paper presents the RECOVER robotic system kinematics, further implemented into the control system, which is described in terms of architecture and functionality. Through a battery of experimental tests, achieved in laboratory conditions using eight healthy subjects, the feasibility and functionality of the proposed robotic system have been validated, and the overall performance of the control system has been studied. The range of motion of each targeted joint has been recorded using a commercially available external sensor system. The kinematic parameters, namely the patient’s joints velocities and accelerations have been recorded and compared to the ones obtained using the virtual model, yielding a very small difference between them, which provides a validation of the RECOVER initial design, both in terms of mechanical construction and control system. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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12 pages, 5118 KiB  
Article
A Pilot Study of Muscle Force between Normal Shoes and Bionic Shoes during Men Walking and Running Stance Phase Using Opensim
by Huiyu Zhou, Datao Xu, Wenjing Quan, Minjun Liang, Ukadike Chris Ugbolue, Julien S. Baker and Yaodong Gu
Actuators 2021, 10(10), 274; https://0-doi-org.brum.beds.ac.uk/10.3390/act10100274 - 18 Oct 2021
Cited by 19 | Viewed by 2666
Abstract
The original idea for bionic shoes (BSs) involves combining the function of unstable foot conditions and the structure of the human plantar. The purpose of this study was to investigate the differences between the normal shoes (NS) and the BS during the stance [...] Read more.
The original idea for bionic shoes (BSs) involves combining the function of unstable foot conditions and the structure of the human plantar. The purpose of this study was to investigate the differences between the normal shoes (NS) and the BS during the stance phases of walking and running. A total of 15 Chinese males from Ningbo University were recruited for this study (age: 24.3 ± 2.01 years; height: 176.25 ± 7.11 cm, body weight (BW): 75.75 ± 8.35 kg). The participants were asked to perform a walking and running task. Statistical parametric mapping (SPM) analysis was used to investigate any differences between NSs and BSs during the walking and running stance phases. The results demonstrated that there were significant differences found (21.23–28.24%, p = 0.040; 84.47–100%, p = 0.017) in hip extension and flexion between the NS and the BS during the walking stance phase. There were no significant differences found in ankle and moment during the running stance phase. Significant differences were found in the rectus femoris (5.29–6.21%; p = 0.047), tibialis anterior (14.37–16.40%; p = 0.038), and medial gastrocnemius (25.55–46.86%; p < 0.001) between the NS and the BS during the walking stance phase. Significant differences were found in rectus femoris (12.83–13.10%, p = 0.049; 15.89–80.19%, p < 0.001), tibialis anterior (15.85–18.31%, p = 0.039; 21.14–24.71%, p = 0.030), medial gastrocnemius (80.70–90.44%; p = 0.007), and lateral gastrocnemius (11.16–27.93%, p < 0.001; 62.20–65.63%, p = 0.032; 77.56–93.45%, p < 0.001) between the NS and the BS during the running stance phase. These findings indicate that BSs are more efficient for muscle control than unstable shoes and maybe suitable for rehabilitation training. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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16 pages, 3929 KiB  
Article
A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator
by Yangyang Hou, Huajie Hong, Dasheng Xu, Zhe Zeng, Yaping Chen and Zhaoyang Liu
Actuators 2021, 10(10), 254; https://0-doi-org.brum.beds.ac.uk/10.3390/act10100254 - 29 Sep 2021
Cited by 1 | Viewed by 1684
Abstract
Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and so on. [...] Read more.
Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and so on. However, the converging achievement of DRL often requires extensive collected data sets and training episodes, which is data inefficient and computing resource consuming. Motivated by the above problem, in this paper, we propose a Twin-Delayed Deep Deterministic Policy Gradient algorithm with a Rebirth Mechanism, Tetanic Stimulation and Amnesic Mechanisms (ATRTD3), for continuous control of a multi-DOF manipulator. In the training process of the proposed algorithm, the weighting parameters of the neural network are learned using Tetanic stimulation and Amnesia mechanism. The main contribution of this paper is that we show a biomimetic view to speed up the converging process by biochemical reactions generated by neurons in the biological brain during memory and forgetting. The effectiveness of the proposed algorithm is validated by a simulation example including the comparisons with previously developed DRL algorithms. The results indicate that our approach shows performance improvement in terms of convergence speed and precision. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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24 pages, 6239 KiB  
Article
Feedback Linearization of Inertially Actuated Jumping Robots
by Adam Cox, Pouria Razzaghi and Yildirim Hurmuzlu
Actuators 2021, 10(6), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/act10060114 - 29 May 2021
Cited by 3 | Viewed by 2831
Abstract
Inertially Actuated Jumping Robots (IAJR) provide a promising new means of locomotion. The difficulty of IAJR is found in the hybrid nature of the ground contact/flying dynamics. Recent research studies in our Systems Lab have provided a family tree of inertially actuated locomotion [...] Read more.
Inertially Actuated Jumping Robots (IAJR) provide a promising new means of locomotion. The difficulty of IAJR is found in the hybrid nature of the ground contact/flying dynamics. Recent research studies in our Systems Lab have provided a family tree of inertially actuated locomotion systems. The proposed Tapping Robot is the most prompt member of this tree. In this paper, a feedback linearization controller is introduced to provide controllability given the 3-dimensional motion complexity. The research objective is to create a general controller that can regulate the locomotion of Inertially Actuated Jumping Robots. The expected results can specify a desired speed and/or jump height, and the controller ensures the desired values are achieved. The controller can achieve the greatest response for the Basketball Robot at a maximum jump height of 0.25 m, which is greater than the former performance with approximately 0.18 m. The design paradigm used on the Basketball Robot was extended to the Tapping Robot. The Tapping Robot achieved a stable average forward velocity of 0.0773 m/s in simulation and 0.157 m/s in experimental results, which is faster than the forward velocity of former robot, Pony III, with 0.045 m/s. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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20 pages, 4671 KiB  
Article
A Non-Linear Continuous-Time Generalized Predictive Control for a Planar Cable-Driven Parallel Robot
by Fouad Inel, Ali Medjbouri and Giuseppe Carbone
Actuators 2021, 10(5), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/act10050097 - 04 May 2021
Cited by 6 | Viewed by 3149
Abstract
This paper addresses a novel nonlinear algorithm for the trajectory tracking of a planar cable-driven parallel robot. In particular, we outline a nonlinear continuous-time generalized predictive control (NCGPC). The proposed controller design is based on the finite horizon continuous-time minimization of a quadratic [...] Read more.
This paper addresses a novel nonlinear algorithm for the trajectory tracking of a planar cable-driven parallel robot. In particular, we outline a nonlinear continuous-time generalized predictive control (NCGPC). The proposed controller design is based on the finite horizon continuous-time minimization of a quadratic predicted cost function. The tracking error in the receding horizon is approximated using a Taylor-series expansion. The main advantage of the proposed NCGPC is based on using an analytic solution, which can be truncated to a desired degree of order of the Taylor-series. This allows us to achieve a prediction horizon of NCGPC tracking performance. The description of the proposed NCGPC method is followed by a comparison between NCGPC and a conventional computed torque control (CTC) method. Robustness tests are performed by considering payload and parameter uncertainties for both controllers. Simulation results of NCGPC compared to the commonly used CTC prove the effectiveness and advantages of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
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