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 539

Special Issue Editors


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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

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

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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

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Keywords

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

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Published Papers (1 paper)

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Research

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 259
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)
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