Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
3.4
Journals
Biomimetics
Special Issues
Advanced Service Robots: Exoskeleton Robots 2025
share announcement

Advanced Service Robots: Exoskeleton Robots 2025

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Locomotion and Bioinspired Robotics".

Deadline for manuscript submissions: 1 October 2025 | Viewed by 878

Special Issue Editors

Dr. Xiaojie Wang

E-Mail Website
Guest Editor
Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei, China
Interests: bio-inspired robots; smart materials; sensors/actuators; mechatronic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yitong Zhou

E-Mail Website
Guest Editor
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
Interests: soft robotics; wearable robotics; service robotics.
Dr. Chanchan Xu

E-Mail
Guest Editor
School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, China
Interests: exoskeleton rehabilitation robot; dexterous hand; soft actuation

Special Issue Information

Dear Colleagues,

This Special Issue aims to present the latest research advances and future trends in the development of exoskeleton robots for motion assistance and rehabilitation. Exoskeleton robots have the potential to revolutionize various industries, such as healthcare, rehabilitation, and industry, by augmenting human capabilities and enabling new applications. Despite the significant technological and scientific achievements in the field of exoskeleton technologies over the last few decades, the evaluation of exoskeleton robots is still an open challenge without a unified standard.

Advanced learning and control technologies applied to physical human–robot interaction represent an interdisciplinary research field aiming to improve interaction between humans and robots. This topic covers a wide range of research areas, including robotics, sensor technology, mechanics, human–robot interaction, Artificial Intelligence, computer vision, and advanced control technology. The new developments under this topic play significant roles in robotic applications in both industry and other sectors, making robots work more efficiently, safely, and reliably. Intelligence is one of the most important key features of wearable robots.

This issue will highlight the interdisciplinary nature of wearable robotics and explore topics such as sensor technologies, bio-inspired design, human–robot interaction, control algorithms, real-world applications, and future directions. It aims to bring together researchers, practitioners, and industry experts to share their findings and insights, and discuss the significance and potential of wearable robots in enhancing human–robot collaboration.

  • Relevant topics in academic research and industry include, but are not limited to, the following:
  • Wearable robots for assistance, augmentation, and rehabilitation of human movements;
  • The design of a lightweight wearable robot;
  • Intelligent control algorithms for exoskeleton robots;
  • Multi-mode information sensing;
  • Human–robot mixed intelligence;
  • Wearable sensors for exoskeleton robots;
  • Pattern recognition and machine learning for exoskeleton robots;
  • Intelligent recognition algorithm based on EMG/EEG signals.

Dr. Xiaojie Wang
Dr. Yitong Zhou
Dr. Chanchan Xu
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. Biomimetics 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 2200 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

  • brain–computer interface
  • exoskeleton robots
  • human–robot interaction
  • intelligent control
  • sensing
  • EMG/EEG
  • human motion recognition
  • wearable robot
  • bio-inspired design

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 6031 KiB  
Article
Control Method in Coordinated Balance with the Human Body for Lower-Limb Exoskeleton Rehabilitation Robots
by Li Qin, Zhanyi Xing, Jianghao Wang, Guangtong Lu and Houzhao Ji
Biomimetics 2025, 10(5), 324; https://doi.org/10.3390/biomimetics10050324 - 16 May 2025
Viewed by 38
Abstract
Ground walking training using a floating-base lower-limb exoskeleton rehabilitation robot improves patients’ dynamic balance function, thereby increasing their motor and daily life activity capabilities. We propose a balance-directed motion generator (BDMG) based on the principles of deep reinforcement learning. The reward function sub-components [...] Read more.
Ground walking training using a floating-base lower-limb exoskeleton rehabilitation robot improves patients’ dynamic balance function, thereby increasing their motor and daily life activity capabilities. We propose a balance-directed motion generator (BDMG) based on the principles of deep reinforcement learning. The reward function sub-components pertaining to physiological guidance and compliant assistance were designed to explore motion instructions that are harmoniously aligned with the human body’s balance correction mechanisms. To address the sparse rewards resulting from the above design, we introduce a stepwise training method that adjusts the reward function to control the model’s training direction and exploration difficulty. Based on the aforementioned generator, we construct a training and evaluation process database and design an abnormal command recognizer by extracting samples with diverse feature characteristics. Furthermore, we develop a sample generation optimizer to search for the optimal action combination within a closed space defined by abnormal commands and extremum points of physiological trajectories, thereby enabling the design of an abnormal instruction corrector. To validate the proposed approach, we implement a training simulation environment in MuJoCo and conduct experiments on the developed lower-limb exoskeleton system. Full article
(This article belongs to the Special Issue Advanced Service Robots: Exoskeleton Robots 2025)
►▼ Show Figures

Figure 1

20 pages, 4118 KiB  
Article
Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots
by Yuepeng Zhang, Guangzhong Cao, Jun Wu, Bo Gao, Linzhong Xia, Chen Lu and Hui Wang
Biomimetics 2025, 10(5), 311; https://doi.org/10.3390/biomimetics10050311 - 12 May 2025
Viewed by 166
Abstract
To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering [...] Read more.
To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering the feature information of different obstacles and the distance between obstacles and robots, a trajectory planning method based on direct point matching was used to generate offline adjusted gait trajectory libraries and obstacle-crossing gait trajectory libraries. A lower limb exoskeleton robot obstacle-crossing motion decision-making algorithm based on obstacle feature information is proposed by combining gait constraints and motion constraints, enabling it to select appropriate motion trajectories in the trajectory library. The proposed obstacle-crossing-motion method was validated at three distances between the obstacle and the robot and with the feature information of four obstacles. The experimental results show that the proposed method can select appropriate trajectories from the trajectory library based on the detected obstacle feature information and can safely complete obstacle-crossing motions. Full article
(This article belongs to the Special Issue Advanced Service Robots: Exoskeleton Robots 2025)
►▼ Show Figures

Figure 1

24 pages, 3963 KiB  
Article
Development of a Bayesian Network-Based Parallel Mechanism for Lower Limb Gait Rehabilitation
by Huiguo Ma, Yuqi Bao, Chao Jia, Guoqiang Chen, Jingfu Lan, Mingxi Shi, He Li, Qihan Guo, Lei Guan, Shuang Li and Peng Zhang
Biomimetics 2025, 10(4), 230; https://doi.org/10.3390/biomimetics10040230 - 8 Apr 2025
Viewed by 337
Abstract
This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, and loss of motor coordination during rehabilitation. To achieve this, it proposes an innovative design method for a lower limb [...] Read more.
This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, and loss of motor coordination during rehabilitation. To achieve this, it proposes an innovative design method for a lower limb rehabilitation training system based on Bayesian networks and parallel mechanisms. A Bayesian network model is constructed based on expert knowledge and structural mechanics analysis, considering key factors such as rehabilitation scenarios, motion trajectory deviations, and rehabilitation goals. By utilizing the motion characteristics of parallel mechanisms, we designed a rehabilitation training device that supports multidimensional gait correction. A three-dimensional digital model is developed, and multi-posture ergonomic simulations are conducted. The study focuses on quantitatively assessing the kinematic characteristics of the hip, knee, and ankle joints while wearing the device, establishing a comprehensive evaluation system that includes range of motion (ROM), dynamic load, and optimization matching of motion trajectories. Kinematic analysis verifies that the structural design of the device is reasonable, aiding in improving patients’ gait, enhancing strength, and restoring flexibility. The Bayesian network model achieves personalized rehabilitation goal optimization through dynamic probability updates. The design of parallel mechanisms significantly expands the range of joint motion, such as enhancing hip sagittal plane mobility and reducing dynamic load, thereby validating the notable optimization effect of parallel mechanisms on gait rehabilitation. Full article
(This article belongs to the Special Issue Advanced Service Robots: Exoskeleton Robots 2025)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop