Submit to Actuators Review for Actuators Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advanced Technologies and Applications in Robotics

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 28868

Share This Special Issue

Special Issue Editors

Dr. Jing Wang

E-Mail Website
Guest Editor

Department of Computing, Sheffield Hallam University, Sheffield S1 2NY, UK
Interests: computer vision; machine learning

Prof. Dr. Zhijie Xu

E-Mail Website
Guest Editor

Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: computer vision; human–computer interaction; information visualisation

Dr. Zhenyu Lu

E-Mail Website
Guest Editor

Bristol Robotics Laboratory, University of the West of England Bristol, Bristol BS16 1QY, UK
Interests: robot intelligent control; robot skill learning; teleoperation

Dr. Jonathan Gomez

E-Mail Website
Guest Editor

Advanced Manufacturing Research Centre, The University of Sheffield, Rotherham S60 5TZ, UK
Interests: Internet of Things; power electronics; analogue and digital electronics

Special Issue Information

Dear Colleagues,

Robotics integrate a broad spectrum of multidisciplinary areas in computer science and engineering, ranging from fundamental research to real-world practical applications. The large diversity of the design, construction, operation, and use of robots brings both challenges and opportunities to our research community. This Special Issue aims to provide a forum for scientists, engineers, scholars, and students to exchange ideas and update technical knowledge and provide a platform where joint research programmes can be formulated for mutual benefit. The Special Issue welcomes participation and contributions from those involved in both theoretical and practical research on all aspects of robotics.

This Special Issue also cooperates with the twenty-seventh International Conference on Automation and Computing (ICAC2022, http://www.cacsuk.co.uk/index.php/icac2022), held at the University of the West of England, Bristol, UK, on 1–3 September 2022. Authors of outstanding papers on topics related to the Special Issue presented at the conference are invited to submit extended versions of their work to this Special Issue.

Dr. Jing Wang
Prof. Dr. Zhijie Xu
Dr. Zhenyu Lu
Dr. Jonathan Gomez
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

robotics
machine learning
neural network
machine vision
human–machine collaborations
smart sensing technology
intelligent system and automation

Published Papers (13 papers)

Download All Papers

Research

Jump to: Review

18 pages, 4425 KiB

Open AccessArticle

Measuring the Precision of the Oculus Quest 2’s Handheld Controllers

by Diogo Pereira, Vitor Oliveira, João L. Vilaça, Vítor Carvalho and Duarte Duque

Actuators 2023, 12(6), 257; https://doi.org/10.3390/act12060257 - 20 Jun 2023

Cited by 1 | Viewed by 4063

Abstract

Consumer-grade virtual reality systems have become increasingly accessible over the last years, making these great options for psychological and physiological medical use. This paper studies the precision of one available system, the Oculus Quest 2. We investigated studies that approached testing of these types of systems using manual systems and automated systems using robot arms and decided to use the latter method for our evaluation. A setup was created where the robotic arm would perform diverse exercises, with the Quest controller attached to it while the headset was either stationary or being worn by a participant. The results show that these systems are precise enough to measure movements that would not be noticed by therapists during traditional rehabilitation and are therefore adequate for medical use. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

24 pages, 13623 KiB

Open AccessArticle

Data-Driven Robotic Tactile Grasping for Hyper-Personalization Line Pick-and-Place

by Zhen Xie, Josh Ye Seng Chen, Guo Wei Lim and Fengjun Bai

Actuators 2023, 12(5), 192; https://doi.org/10.3390/act12050192 - 1 May 2023

Cited by 3 | Viewed by 2853

Abstract

Industries such as the manufacturing or logistics industry need algorithms that are flexible to handle novel or unknown objects. Many current solutions in the market are unsuitable for grasping these objects in high-mix and low-volume scenarios. Finally, there are still gaps in terms of grasping accuracy and speed that we would like to address in this research. This project aims to improve the robotic grasping capability for novel objects with varying shapes and textures through the use of soft grippers and data-driven learning in a hyper-personalization line. A literature review was conducted to understand the tradeoffs between the deep reinforcement learning (DRL) approach and the deep learning (DL) approach. The DRL approach was found to be data-intensive, complex, and collision-prone. As a result, we opted for a data-driven approach, which to be more specific, is PointNet GPD in this project. In addition, a comprehensive market survey was performed on tactile sensors and soft grippers with consideration of factors such as price, sensitivity, simplicity, and modularity. Based on our study, we chose the Rochu two-fingered soft gripper with our customized force-sensing resistor (FSR) force sensors mounted on the fingertips due to its modularity and compatibility with tactile sensors. A software architecture was proposed, including a perception module, picking module, transfer module, and packing module. Finally, we conducted model training using a soft gripper configuration and evaluated grasping with various objects, such as fast-moving consumer goods (FMCG) products, fruits, and vegetables, which are unknown to the robot prior to grasping. The grasping accuracy was improved from 75% based on push and grasp to 80% based on PointNetGPD. This versatile grasping platform is independent of gripper configurations and robot models. Future works are proposed to further enhance tactile sensing and grasping stability. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

20 pages, 4310 KiB

Open AccessArticle

Learning Pose Dynamical System for Contact Tasks under Human Interaction

by Shangshang Yang, Xiao Gao, Zhao Feng and Xiaohui Xiao

Actuators 2023, 12(4), 179; https://doi.org/10.3390/act12040179 - 20 Apr 2023

Cited by 1 | Viewed by 1609

Abstract

Robots are expected to execute various operation tasks like a human by learning human working skills, especially for complex contact tasks. Increasing demands for human–robot interaction during task execution makes robot motion planning and control a considerable challenge, not only to reproduce demonstration motion and force in the contact space but also to resume working after interacting with a human without re-planning motion. In this article, we propose a novel framework based on a time-invariant dynamical system (DS), taking into account both human skills transfer and human–robot interaction. In the proposed framework, the human demonstration trajectory was modeled by the pose diffeomorphic DS to achieve online motion planning. Furthermore, the motion of the DS was modified by admittance control to satisfy different demands. We evaluated the method with a UR5e robot in the contact task of the composite woven layup. The experimental results show that our approach can effectively reproduce the trajectory and force learned from human demonstration, allow human–robot interaction safely during the task, and control the robot to return to work automatically after human interaction. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

21 pages, 15510 KiB

Open AccessArticle

Automatic Aluminum Alloy Surface Grinding Trajectory Planning of Industrial Robot Based on Weld Seam Recognition and Positioning

by Hong Zhao, Ke Wen, Tianjian Lei, Yinan Xiao and Yang Pan

Actuators 2023, 12(4), 170; https://doi.org/10.3390/act12040170 - 12 Apr 2023

Cited by 4 | Viewed by 2238

Abstract

In this paper, we propose a novel method for planning grinding trajectories on curved surfaces to improve the grinding efficiency of large aluminum alloy surfaces with welds and defect areas. Our method consists of three parts. Firstly, we introduce a deficiency positioning method based on a two-dimensional image and three-dimensional point cloud, which enables us to accurately and quickly locate the three-dimensional defective areas. Secondly, we propose a 2D weld positioning method based on the defect area and obtain the spatial position of the 3D weld by combining the relationship between 2D and 3D images. Additionally, we propose an orthogonal projection method from the point cloud to the aluminum alloy surface to calculate the weld reinforcement. Thirdly, we present a space spiral grinding trajectory planning method for complex curved surfaces based on the characteristics of the weld reinforcement, spatial position, and spatial position information of the defect area. This method shortens the grinding time of the defect area and improves efficiency. Simulation and experimental results show that our grinding trajectory planning method is more efficient than other grinding methods in removing defects from the surface of aluminum alloys. Moreover, the defect area after grinding is smoother than before. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

19 pages, 8583 KiB

Open AccessArticle

Learning-Based Visual Servoing for High-Precision Peg-in-Hole Assembly

by Yue Shen, Qingxuan Jia, Ruiquan Wang, Zeyuan Huang and Gang Chen

Actuators 2023, 12(4), 144; https://doi.org/10.3390/act12040144 - 27 Mar 2023

Cited by 4 | Viewed by 2055

Abstract

Visual servoing is widely used in the peg-in-hole assembly due to the uncertainty of pose. Humans can easily align the peg with the hole according to key visual points/edges. By imitating human behavior, we propose P2HNet, a learning-based neural network that can directly extract desired landmarks for visual servoing. To avoid collecting and annotating a large number of real images for training, we built a virtual assembly scene to generate many synthetic data for transfer learning. A multi-modal peg-in-hole strategy is then introduced to combine image-based search-and-force-based insertion. P2HNet-based visual servoing and spiral search are used to align the peg with the hole from coarse to fine. Force control is then used to complete the insertion. The strategy exploits the flexibility of neural networks and the stability of traditional methods. The effectiveness of the method was experimentally verified in the D-sub connector assembly with sub-millimeter clearance. The results show that the proposed method can achieve a higher success rate and efficiency than the baseline method in the high-precision peg-in-hole assembly. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

19 pages, 1459 KiB

Open AccessArticle

Uncalibrated Adaptive Visual Servoing of Robotic Manipulators with Uncertainties in Kinematics and Dynamics

by Guanyu Lai, Aoqi Liu, Weijun Yang, Yuanfeng Chen and Lele Zhao

Actuators 2023, 12(4), 143; https://doi.org/10.3390/act12040143 - 27 Mar 2023

Cited by 3 | Viewed by 1376

Abstract

In the study, we propose a novel adaptive visual servoing control scheme for robotic manipulators with kinematic and dynamic uncertainties, where the camera used is uncalibrated, which implies that its intrinsic and extrinsic parameters are unavailable for measurement. For our scheme, a depth-independent composite Jacobian matrix is constructed to make visual parameters and robotic physical parameters appear linearly in a parametrized uniform form so that an adaptive algorithm can be developed to estimate their values. With the raised adaptive algorithm, the potential singularity of the Jacobian matrix can be well circumvented by updating estimated parameters in an appropriate tiny range of actual values. With our scheme, the asymptotic convergence of the image tracking error to zero is established successfully, in addition to the signal boundedness of the closed-loop system. The effectiveness of the proposed scheme is confirmed by simulation results based on a 6-DOF PUMA manipulator. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

21 pages, 6784 KiB

Open AccessArticle

Reinforcement Learning-Based Control of Single-Track Two-Wheeled Robots in Narrow Terrain

by Qingyuan Zheng, Yu Tian, Yang Deng, Xianjin Zhu, Zhang Chen and Bing Liang

Actuators 2023, 12(3), 109; https://doi.org/10.3390/act12030109 - 28 Feb 2023

Cited by 1 | Viewed by 1829

Abstract

The single-track two-wheeled (STTW) robot has the advantages of small size and flexibility, and it is suitable for traveling in narrow terrains of mountains and jungles. In this article, a reinforcement learning control method for STTW robots is proposed for driving fast in narrow terrain with limited visibility and line-of-sight occlusions. The proposed control scheme integrates path planning, trajectory tracking, and balancing control in a single framework. Based on this method, the state, action, and reward function are defined for narrow terrain passing tasks. At the same time, we design the actor network and the critic network structures and use the twin delayed deep deterministic policy gradient (TD3) to train these neural networks to construct a controller. Next, a simulation platform is formulated to test the performances of the proposed control method. The simulation results show that the obtained controller allows the STTW robot to effectively pass the training terrain, as well as the four test terrains. In addition, this article conducts a simulation comparison to prove the advantages of the integrated framework over traditional methods and the effectiveness of the reward function. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

15 pages, 26064 KiB

Open AccessArticle

A Gripper-like Exoskeleton Design for Robot Grasping Demonstration

by Hengtai Dai, Zhenyu Lu, Mengyuan He and Chenguang Yang

Actuators 2023, 12(1), 39; https://doi.org/10.3390/act12010039 - 12 Jan 2023

Cited by 3 | Viewed by 2182

Abstract

Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and to accelerate the development of new robotic grasping tasks. A well-designed demonstration device can effectively represent human grasping motion to transfer grasping skills to robots. In this paper, an improved gripper-like exoskeleton with a data collection system is proposed. First, we present the mechatronic details of the exoskeleton and its motion-tracking system, considering the manipulation flexibility and data acquisition requirements. We then present the capabilities of the device and its data collection system, which collects the position, pose and displacement of the gripper on the exoskeleton. The collected data is further processed by the data acquisition and processing software. Next, we describe the principles of Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) in robot skill learning, which are used to transfer the raw data from demonstrations to robot motions. In the experiment, an optimized trajectory was learned from multiple demonstrations and reproduced on a robot. The results show that the GMR complemented with GMM is able to learn a smooth trajectory from demonstration trajectories with noise. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

16 pages, 16469 KiB

Open AccessArticle

Workspace Description and Evaluation of Master-Slave Dual Hydraulic Manipulators

by Yao Sun, Yi Wan, Haifeng Ma and Xichang Liang

Actuators 2023, 12(1), 9; https://doi.org/10.3390/act12010009 - 23 Dec 2022

Cited by 2 | Viewed by 2365

Abstract

Nuclear power plant emergency robots are robots used to respond to significant public safety incidents, such as uncontrolled radioactive sources and nuclear catastrophe leaks. However, there are no standardized evaluation criteria for the optimal design of the robots. We offer a quantitative analytic algorithm for optimizing nuclear power plant emergency robots to address this issue. The method optimizes the structural parameters of the robot in accordance with the workspace by analyzing, comparing, and evaluating the workspace. The approach comprises constructing a kinematic model of the mechanical arm and proposing an optimization algorithm based on the alpha shape to accurately describe the manipulator workspace; employing the proposed convex hull algorithm to quantitatively analyze and evaluate the workspace generated by different solutions in terms of area, volume, task demand, Structural Length Index and Global Conditioning Index; and determining the robotic arm joint parameters by selecting the optimum workspace design solution. Using the suggested algorithm, we optimize the design of the master and slave robotic arms of the nuclear power plant emergency robots. Theoretical calculations and simulation results demonstrate that the method is an effective and practical evaluation technique that not only accurately describes the workspace but also optimizes the design of the nuclear power plant emergency robots. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

18 pages, 4658 KiB

Open AccessArticle

Fixed-Time Incremental Neural Control for Manipulator Based on Composite Learning with Input Saturation

by Yanli Fan, Haiqi Huang and Chenguang Yang

Actuators 2022, 11(12), 373; https://doi.org/10.3390/act11120373 - 10 Dec 2022

Cited by 1 | Viewed by 1504

Abstract

In this paper, an adaptive incremental neural network (INN) fixed-time tracking control scheme based on composite learning is investigated for robot systems under input saturation. Firstly, by integrating the composite learning method into the INN to cope with the inevitable dynamic uncertainty, a novel adaptive updating law of NN weights is designed, which does not need to satisfy the stringent persistent excitation (PE) conditions. In addition, for the saturated input, differing from adding the auxiliary system, this paper introduces a hyperbolic tangent function to deal with the saturation nonlinearity by converting the asymmetric input constraints into the symmetric ones. Moreover, the fixed-time control approach and Lyapunov theory are combined to ensure that all the signals of the robot closed-loop control systems converge to a small neighborhood of the origin in a fixed time. Finally, numerical simulation results verify the effectiveness of the fixed-time control and composite learning algorithm. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

14 pages, 46301 KiB

Open AccessArticle

Design and Implementation of an Asynchronous Finite State Controller for Wheeled Mobile Robots

by Alessandro Bozzi, Simone Graffione, Roberto Sacile and Enrico Zero

Actuators 2022, 11(11), 330; https://doi.org/10.3390/act11110330 - 13 Nov 2022

Cited by 3 | Viewed by 1516

Abstract

Wheeled mobile robots (WMRs) can navigate in uncontrolled environments with the assistance of electronic or physical devices. Several works have been conducted on the control and management of the path-tracking of a vehicle in different road scenarios. This paper aims to create an asynchronous finite state control law for a WMR. The control law is based on a proportional–integral–derivative controller, and the performance of the proposed model is evaluated in virtual and real environments in two different scenarios. In the first one, the WMR must perform a zig-zag maneuver between obstacles, while the second one involves a double left lane change. In the proposed scenarios, the WMR drives along a path until an obstacle is detected at less than 50 cm, causing the WMR to check whether the first lane is free to go and move on. These scenarios and the related required engineering approaches seem particularly suitable for system engineering in a student’s laboratory for the design and implementation of automated guidance system modeling. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

18 pages, 2706 KiB

Open AccessArticle

An Intelligent Tracking System for Moving Objects in Dynamic Environments

by Nada Ali Hakami, Hanan Ahmed Hosni Mahmoud and Abeer Abdulaziz AlArfaj

Actuators 2022, 11(10), 274; https://doi.org/10.3390/act11100274 - 25 Sep 2022

Cited by 1 | Viewed by 1463

Abstract

Localization of suspicious moving objects in dynamic environments requires high accuracy mapping. A deep learning model is proposed to track crossing moving objects in the opposite direction. Moving objects locus measurements are computed from the space included in the boundaries of the images in the intersecting cameras. Object appearance is designated by the color and textural histograms in the intersecting camera views. The incorrect mapping of moving objects in a dynamic environment through synchronized localization can be considerably increased in complex areas. This is done due to the presence of unfit points that are triggered by moving targets. To face this problem, a robust model using the dynamic province rejection technique (DPR) is presented. We are proposing a novel model that incorporates a combination of the deep learning method and a tracking system that rejects dynamic areas which are not within the environment boundary of interest. The technique detects the dynamic points from sequential video images and partitions the current video image into super blocks and tags the border differences. In the last stage, dynamic areas are computed from dynamic points and superblock boundaries. Static regions are utilized to compute the positions to enhance the path computation precision of the model. Simulation results show that the introduced model has better performance than the state-of-the-art similar models in both the VID and MOVSD4 datasets and is higher than the state-of-the-art tracking systems with better speed performance. The experiments prove that the computed path error in the dynamic setting can be decreased by 81%. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures

Figure 1

Review

Jump to: Research

18 pages, 1122 KiB

Open AccessReview

An Interdisciplinary Approach and Advanced Techniques for Enhanced 3D-Printed Upper Limb Prosthetic Socket Design: A Literature Review

by Kai Xu and Shengfeng Qin

Actuators 2023, 12(6), 223; https://doi.org/10.3390/act12060223 - 27 May 2023

Cited by 6 | Viewed by 2128

Abstract

This review investigates the opportunities and challenges of interdisciplinary research in upper limb prosthetic (ULP) socket design and manufacturing, which is crucial for improving the lives of individuals with limb loss. By integrating various disciplines, such as engineering, materials science, biomechanics, and health care, with emerging technologies such as 3D printing, artificial intelligence (AI), and virtual reality (VR), interdisciplinary collaboration can foster innovative solutions tailored to users’ diverse needs. Despite the immense potential, interdisciplinary research faces challenges in effective communication, collaboration, and evaluation. This review analyses pertinent case studies and discusses the implications of interdisciplinary research, emphasizing the importance of fostering a shared understanding, open communication, and institutional innovation. By examining technological advancements, user satisfaction, and prosthetic device usage in various interdisciplinary research examples, invaluable insights and direction for researchers and professionals seeking to contribute to this transformative field are provided. Addressing the challenges and capitalizing on the opportunities offered by interdisciplinary research can significantly improve upper limb prosthetic socket design and manufacturing, ultimately enhancing the quality of life for users worldwide. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Robotics)

► Show Figures