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Biomimetics
Special Issues
Bio-Inspired Robotics and Applications
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Bio-Inspired Robotics and Applications

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

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 6998

Special Issue Editors

Dr. Junfei Li

E-Mail Website
Guest Editor
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: robotic collectives; swarm robots; bio-inspired algorithms; human–robot collaborations
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Simon X. Yang

E-Mail Website
Guest Editor
Advanced Robotics & Intelligent Systems (ARIS) Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: intelligent systems; robotics; control systems; sensors and multi-sensor fusion; wireless sensor networks; intelligent communications; intelligent transportation; machine learning; computational neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biological inspiration provides the basis for many aspects of robotics. The resourceful methodologies of biological organisms have been incorporated in the development of many new methodologies, strategies, and algorithms for robotic systems. The novelty and significance of this new research has provided new knowledge to the respective research communities, which could potentially have many civilian and military applications.

The main goal of this Special Issue is to investigate the fundamental theories of bio-inspired robotics methodologies and to report their novel applications in the field of robotics, such as real-time sensing and multi-sensor fusion, real-time intelligent navigation, cooperation of multiple robotic systems, simultaneous localization and mapping (SLAM), real-time collision-free path planning, and tracking and control of the robot.

This Special Issue invites original research and review articles that contribute new knowledge to their respective fields of study. It also aims to provide insights into biologically inspired methodologies that can be applied across various research areas and applications.

Dr. Junfei Li
Prof. Dr. Simon X. Yang
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

  • autonomous robotic systems
  • intelligent systems
  • bio-inspired intelligence
  • intelligent control systems
  • intelligent multi-sensor fusion
  • intelligent path planning and tracking
  • intelligent real-time navigation
  • intelligent coordination and cooperation
  • intelligent robot teleoperation

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Related Special Issue

Published Papers (7 papers)

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Research

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16 pages, 1118 KiB  
Article
Analysis of Torsional Response in Pneumatic Artificial Muscles
by Frank C. Cianciarulo, Eric Y. Kim and Norman M. Wereley
Biomimetics 2025, 10(3), 139; https://doi.org/10.3390/biomimetics10030139 - 25 Feb 2025
Viewed by 439
Abstract
Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a helical braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are advantageous because of their high specific work and specific power, as well as their ability [...] Read more.
Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a helical braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are advantageous because of their high specific work and specific power, as well as their ability to produce large axial displacements. The axial and radial behavior of PAMs have been well studied. The torsional response of PAMs have not been explored before. Accurate prediction of the torsional force was desired for use in a bio-inspired worm-like robot capable of using an auger mounted to a PAM to bore out tunnels. Thus, an understanding of torsional response was a key objective. Modeling of the torsional response was performed using a force balance approach, and multiple model variations were considered, such as St. Venant’s torsion, bladder buckling, and asymmetrical braid loading. Torsional testing was performed to validate the model using a custom torsional testing system. Data from the tests was compared to the predicted torsional response. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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19 pages, 1222 KiB  
Article
Unmanned-Aerial-Vehicle Trajectory Planning for Reliable Edge Data Collection in Complex Environments
by Zhengzhe Xiang, Fuli Ying, Xizi Xue, Xiaorui Peng and Yufei Zhang
Biomimetics 2025, 10(2), 109; https://doi.org/10.3390/biomimetics10020109 - 12 Feb 2025
Viewed by 662
Abstract
With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face [...] Read more.
With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV’s ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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21 pages, 7597 KiB  
Article
A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection
by Dabing Peng, Junfeng Cai, Lu Zheng, Minghong Li, Ling Nie and Zuojin Li
Biomimetics 2025, 10(2), 104; https://doi.org/10.3390/biomimetics10020104 - 12 Feb 2025
Viewed by 659
Abstract
Mountainous roads are severely affected by environmental factors such as insufficient lighting and shadows from tree branches, which complicates the detection of drivers’ facial features and the determination of fatigue states. An improved method for recognizing driver fatigue states on mountainous roads using [...] Read more.
Mountainous roads are severely affected by environmental factors such as insufficient lighting and shadows from tree branches, which complicates the detection of drivers’ facial features and the determination of fatigue states. An improved method for recognizing driver fatigue states on mountainous roads using the YOLOv5 neural network is proposed. Initially, modules from Deformable Convolutional Networks (DCNs) are integrated into the feature extraction stage of the YOLOv5 framework to improve the model’s flexibility in recognizing facial characteristics and handling postural changes. Subsequently, a Triplet Attention (TA) mechanism is embedded within the YOLOv5 network to bolster image noise suppression and improve the network’s robustness in recognition. Finally, the Wing loss function is introduced into the YOLOv5 model to heighten the sensitivity to micro-features and enhance the network’s capability to capture details. Experimental results demonstrate that the modified YOLOv5 neural network achieves an average accuracy rate of 85% in recognizing driver fatigue states. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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13 pages, 3886 KiB  
Article
Muscle Activation Reduction During Walking with an Active Hip Exoskeleton
by Wentao Sheng, Farzan Ghalichi, Li Ding, Chengtao Yu, Mingyue Lu and Xia Ye
Biomimetics 2025, 10(1), 24; https://doi.org/10.3390/biomimetics10010024 - 3 Jan 2025
Viewed by 1084
Abstract
Objective: To reduce hip joint muscles’ activation during walking with an active hip exoskeleton. Background: Few studies examine the optimal active assistance timing of the hip exoskeleton based on muscle activation characteristics. Methods: Sixteen gender-balanced healthy adults (mean age 28.8 years) performed four [...] Read more.
Objective: To reduce hip joint muscles’ activation during walking with an active hip exoskeleton. Background: Few studies examine the optimal active assistance timing of the hip exoskeleton based on muscle activation characteristics. Methods: Sixteen gender-balanced healthy adults (mean age 28.8 years) performed four tasks (each over 20 min). Tasks were different in loading and assistance. Muscle activation was collected by surface electromyography. The collected oxygen consumption evaluated the performance of the proposed active assistance strategy. Results: Experimental results verified that lower muscle activation and metabolism could be achieved when the active assistance gait phase was 9–60% of the gait cycle than that of all-gait-cycle active assist. Conclusions: Regulating the exoskeleton’s active assistance timing according to muscles’ activation characteristics can improve functional assistance. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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19 pages, 3339 KiB  
Article
Trajectory Tracking Control for Robotic Manipulator Based on Soft Actor–Critic and Generative Adversarial Imitation Learning
by Jintao Hu, Fujie Wang, Xing Li, Yi Qin, Fang Guo and Ming Jiang
Biomimetics 2024, 9(12), 779; https://doi.org/10.3390/biomimetics9120779 - 21 Dec 2024
Cited by 1 | Viewed by 1131
Abstract
In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic [...] Read more.
In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic model of the manipulator. Specifically, it limits the torque and joint angle to a certain range. Firstly, in order to cope with the instability problem during training and obtain a stability policy, soft actor–critic (SAC) and LSTM are combined. The changing trends of joint position over time are more comprehensively captured and understood by employing an LSTM architecture designed for robotic manipulator systems, thereby reducing instability during the training of robotic manipulators for tracking control tasks. Secondly, the obtained policy by SAC-LSTM is used as expert data for GAIL to learn a better control policy. This SAC-LSTM-GAIL (SL-GAIL) algorithm does not need to spend time exploring unknown environments and directly learns the control strategy from stable expert data. Finally, it is demonstrated by the simulation results that the end effector of the robot tracking task is effectively accomplished by the proposed SL-GAIL algorithm, and more superior stability is exhibited in a test environment with interference compared with other algorithms. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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20 pages, 22712 KiB  
Article
Adaptive Route Memory Sequences for Insect-Inspired Visual Route Navigation
by Efstathios Kagioulis, James Knight, Paul Graham, Thomas Nowotny and Andrew Philippides
Biomimetics 2024, 9(12), 731; https://doi.org/10.3390/biomimetics9120731 - 1 Dec 2024
Viewed by 1078
Abstract
Visual navigation is a key capability for robots and animals. Inspired by the navigational prowess of social insects, a family of insect-inspired route navigation algorithms—familiarity-based algorithms—have been developed that use stored panoramic images collected during a training route to subsequently derive directional information [...] Read more.
Visual navigation is a key capability for robots and animals. Inspired by the navigational prowess of social insects, a family of insect-inspired route navigation algorithms—familiarity-based algorithms—have been developed that use stored panoramic images collected during a training route to subsequently derive directional information during route recapitulation. However, unlike the ants that inspire them, these algorithms ignore the sequence in which the training images are acquired so that all temporal information/correlation is lost. In this paper, the benefits of incorporating sequence information in familiarity-based algorithms are tested. To do this, instead of comparing a test view to all the training route images, a window of memories is used to restrict the number of comparisons that need to be made. As ants are able to visually navigate when odometric information is removed, the window position is updated via visual matching information only and not odometry. The performance of an algorithm without sequence information is compared to the performance of window methods with different fixed lengths as well as a method that adapts the window size dynamically. All algorithms were benchmarked on a simulation of an environment used for ant navigation experiments and showed that sequence information can boost performance and reduce computation. A detailed analysis of successes and failures highlights the interaction between the length of the route memory sequence and environment type and shows the benefits of an adaptive method. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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Review

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29 pages, 7270 KiB  
Review
Nature-Inspired Solutions for Sustainable Mining: Applications of NIAs, Swarm Robotics, and Other Biomimicry-Based Technologies
by Joven Tan, Noune Melkoumian, David Harvey and Rini Akmeliawati
Biomimetics 2025, 10(3), 181; https://doi.org/10.3390/biomimetics10030181 - 14 Mar 2025
Viewed by 870
Abstract
Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It [...] Read more.
Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It presents a systematic classification of each methodology, emphasizing their key advantages and disadvantages as well as considering real-life mining application scenarios, including hazard detection, autonomous transportation and energy-efficient drilling. Case studies are citied to demonstrate how these methodologies work together, and an extensive comparison table considering their applications at mines, such as Boliden, Diavik Diamond Mine, Olympic Dam and others, presents a summary of their scalability and practicality. This paper highlights future directions such as multi-robot coordination and hybrid NIAs, to improve operational resilience and sustainability. It also provides a broad overview of biomimicry and critically examines unresolved issues like real-time adaptation, parameter tuning and mechanical wear. The paper aims to offer a comprehensive insight into using bio-inspired models to enhance mining efficiency, safety and environmental management, while proposing a road map for resolving the issues that continue to be a hurdle for wide adaptation of these technologies in the mining industry. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications)
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