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Advances in Robotics and Autonomous Systems
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Advances in Robotics and Autonomous Systems

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6444

Special Issue Editors

Dr. Zhufeng Shao

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Interests: parallel mechanisms; cable-driven parallel robots; soft robots; the innovative design of robots; humanoid and rehabilitation robots; autonomous systems and intelligent manufacturing
Prof. Dr. Fumin Zhang

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Interests: marine robotics and autonomous systems; mobile sensor and actuator networks; bio-inspired distributed active perception; human–autonomy interaction and integration

Special Issue Information

Dear Colleagues,

Since the third industrial revolution, robots and automation systems have been continuously integrated into human production and life, improving efficiency, quality, and convenience. With the development of artificial intelligence and Internet of Things technology, we penetrated the fourth industrial revolution, or Industry 4.0. As the main force of the fourth industrial revolution, robots will collaborate more extensively and deeply with humans and become the support for social development, making the world a better place day by day.

In this process, the core technologies of robots and automation systems are rapidly iterated and broken through, while multi-disciplinary integration promotes the application of robots. This special issue provides a forum for researchers on robotics, intelligent equipment, and autonomous technology to share the latest achievements and establish potential cooperation, welcoming new research results from academia and industry. This special issue is dedicated to the state-of-the-art and future needs in several areas related to equipment and technology to monitor, control, and operate any process or function with accuracy and efficiency including, but not limited to, robotics and mechatronics, artificial intelligence, design, modeling, control logic, and sensors. Cable-driven robots, parallel and hybrid mechanisms, soft robots, mobile robots, aerial and underwater robots, exoskeletons, and rehabilitation robots in use today or in the near future will be discussed. Papers are welcome on topics related to aspects of theory, practice, and application.

In particular, the topics of interest include, but are not limited to:

  • Robotics and mechatronics;
  • Cable-driven robots, parallel and hybrid mechanism;
  • Mobile, aerial, and marine robotics;
  • Bionic, humanoid, rehabilitation, and exoskeleton robots;
  • Control theory, systems, and applications;
  • Human–autonomy interaction, integration, and safety;
  • Haptics and haptic interfaces;  
  • Positioning, path planning, scheduling, and trajectory; 
  • Machine learning, machine vision, and artificial intelligence.

Dr. Zhufeng Shao
Prof. Dr. Fumin Zhang
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. 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
  • mechatronics
  • artificial Intelligence
  • control
  • application

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Published Papers (5 papers)

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Research

28 pages, 25158 KiB  
Article
A Machine Learning-Based Study on the Demand for Community Elderly Care Services in Central Urban Areas of Major Chinese Cities
by Fang Wen, Zihao Liu, Bo Zhang, Yan Zhang, Ziqi Zhang and Yuyang Zhang
Appl. Sci. 2025, 15(8), 4141; https://doi.org/10.3390/app15084141 - 9 Apr 2025
Viewed by 275
Abstract
China’s population is aging rapidly, with a large proportion of elderly individuals “aging in place”. In central areas of large cities, the amount of community and home-based elderly care services provided by the government and for-profit organizations are insufficient to meet the demands [...] Read more.
China’s population is aging rapidly, with a large proportion of elderly individuals “aging in place”. In central areas of large cities, the amount of community and home-based elderly care services provided by the government and for-profit organizations are insufficient to meet the demands of these “aging in place” elderly. Taking the core area of Beijing as the spatial scope, this empirical study collects the demand on services of the main types of elderly residents in community and home-based dwelling through questionnaires (n = 242) and employs a mixed-methods approach for analysis. Descriptive statistics and exploratory factor analysis are used to determine the categories and levels of those demands, and machine learning methods (random forest regression model) are used to calculate the importance of various influencing factors (features of the elderly and subdistricts’ built environment) on them. It is shown that elderly residents have a higher demand for psychological and physical condition maintenance services (mean = 3.40), and a lower demand for reconciliation and rights defense services (mean = 3.08). The results also show that the built environment factors are very important for the elderly on choosing demands, especially mean distance of CECSs (community elderly care stations) to downtown landmarks and main roads in subdistricts, and characteristics of CECS. The elderly’s own features also have a relatively important impact, especially their living arrangements, caregivers, and occupations before retirement. This study applies machine learning techniques to sociological survey analysis, helping to understand the intensity of elderly people’s demand for various community and home-based elderly care services. It provides a reference for the allocation of such service resources. Full article
(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)
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18 pages, 29591 KiB  
Article
Experimental Evaluation of Precise Placement with Pushing Primitive Based on Cartesian Force Control
by Jinseong Park, Jeong-Jung Kim and Doo-Yeol Koh
Appl. Sci. 2025, 15(1), 387; https://doi.org/10.3390/app15010387 - 3 Jan 2025
Viewed by 793
Abstract
In-hand manipulation with Cartesian-force-control-based pushing primitives is introduced to achieve the precise placement of an object in a desired position at a manufacturing site. In the bin picking process, achieving the desired grasping posture is challenging due to limitations in the sensing and [...] Read more.
In-hand manipulation with Cartesian-force-control-based pushing primitives is introduced to achieve the precise placement of an object in a desired position at a manufacturing site. In the bin picking process, achieving the desired grasping posture is challenging due to limitations in the sensing and control of the robotic arm, interference from clustered objects, and unintended collisions, which hinder achieving the planned pose. Even under such conditions, in cases that require precise operations, such as manufacturing processes, maintaining a desired placement posture is crucial for the precise placement of objects into the machine slot. In this paper, a pushing primitive incorporating force feedback control is applied to ensure that the gripper is consistently positioned at the edge of the grasped object regardless of the initial grasping position by utilizing the surrounding environment of the processing machine. Modeling the exact contact friction between the gripper and the grasped object is challenging; therefore, instead of relying on a motion planning approach, we addressed the problem using a control method that leverages feedback from the external force information of the robot manipulator. Additional sensors such as external cameras or tactile sensors in the gripper are not required. The pushing primitive is executed by applying a force greater than the frictional force between the gripper and the grasped object, leveraging the surrounding environment. Experimental verification confirmed that the proposed method achieves precise placement into the machine slot, regardless of initial grasping positions. It also proved to be effective on an actual testbed. Full article
(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)
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20 pages, 2696 KiB  
Article
See-Then-Grasp: Object Full 3D Reconstruction via Two-Stage Active Robotic Reconstruction Using Single Manipulator
by Youngtaek Hong, Jonghyeon Kim, Geonho Cha, Eunwoo Kim and Kyungjae Lee
Appl. Sci. 2025, 15(1), 272; https://doi.org/10.3390/app15010272 - 30 Dec 2024
Viewed by 1209
Abstract
In this paper, we propose an active robotic 3D reconstruction methodology for achieving full object 3D reconstruction. Existing robotic 3D reconstruction approaches often struggle to cover the entire view space of the object or reconstruct occluded regions, such as the bottom or back [...] Read more.
In this paper, we propose an active robotic 3D reconstruction methodology for achieving full object 3D reconstruction. Existing robotic 3D reconstruction approaches often struggle to cover the entire view space of the object or reconstruct occluded regions, such as the bottom or back side. To address these limitations, we introduce a two-stage robotic active 3D reconstruction pipeline, named See-Then-Grasp (STG), that employs a robot manipulator for direct interaction with the object. The manipulator moves toward the points with the highest uncertainty, ensuring efficient data acquisition and rapid reconstruction. Our method expands the view space of the object to include the entire perspective, including occluded areas, making the previous fixed view candidate approach time-consuming for identifying uncertain regions. To overcome this, we propose a gradient-based next best view pose optimization method that efficiently identifies uncertain regions, enabling faster and more effective reconstruction. Our method optimizes the camera pose based on an uncertainty function, allowing it to identify the most uncertain regions in a short time. Through experiments with synthetic objects, we demonstrate that our approach effectively addresses the next best view selection problem, achieving significant improvements in computational efficiency while maintaining high-quality 3D reconstruction. Furthermore, we validate our method on a real robot, showing that it enables full 3D reconstruction of real-world objects. Full article
(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)
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19 pages, 2267 KiB  
Article
Cost-Effective Localization of Mobile Robots Using Ultrasound Beacons and Differential Time-of-Flight Measurement
by Basil Mohammed Al-Hadithi and Carlos Pastor
Appl. Sci. 2024, 14(17), 7597; https://doi.org/10.3390/app14177597 - 28 Aug 2024
Cited by 1 | Viewed by 1558
Abstract
This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between [...] Read more.
This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between the robot and optimally placed beacons. Unlike other ToF methods that require synchronization pulses, the proposed approach eliminates this requirement, significantly simplifying the setup and reducing system complexity. Furthermore, the system achieves a higher sampling rate than conventional synchronization-based systems, enhancing real-time performance. Detailed analysis and simulation demonstrate the system’s ability to provide accurate and reliable localization. The results highlight the potential for broad application in various robotic environments, offering a robust solution for absolute positioning without complex synchronization strategies. This work underscores the advantages of using ToF measurements with ultrasound beacons and contributes to the ongoing development of efficient and cost-effective robotic localization systems. Full article
(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)
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19 pages, 17672 KiB  
Article
Kinematic Parameter Identification for a Parallel Robot with an Improved Particle Swarm Optimization Algorithm
by Dayong Yu
Appl. Sci. 2024, 14(15), 6557; https://doi.org/10.3390/app14156557 - 26 Jul 2024
Cited by 1 | Viewed by 1164
Abstract
The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. [...] Read more.
The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. A novel kinematic parameter identification method with an improved particle swarm optimization (PSO) algorithm is proposed to enhance positioning and orientation accuracy of the parallel robot. A fitness function is established using these residuals between the measured and computed poses by a coordinate measuring machine and forward kinematics. The kinematic parameter identification problem is turned into a high-dimensional nonlinear optimization in which the unknown kinematic parameter errors are regarded as optimal variables. The optimal variables are solved by the proposed improved PSO algorithm. The mean values of the positioning and orientation errors are reduced from 4.3268 mm and 0.2221 deg to 0.7692 mm and 0.0674 deg, respectively. The proposed kinematic parameter identification method increases the positioning accuracy mean by 22.26% and the orientation accuracy mean by 32.80% compared with the least squares method. The kinematic parameter identification method with the improved PSO algorithm can effectively enhance positioning and orientation accuracy of the parallel robot for docking motion simulation. Full article
(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)
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