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Biomimetics
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Biologically Inspired Design and Control of Robots: Second Edition
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Biologically Inspired Design and Control of Robots: Second Edition

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 8171

Special Issue Editors

Prof. Dr. Peng Li

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, China
Interests: miniature robot; morphing mechanism; mechanism design; metamorphous multirotor
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yantao Shen

E-Mail Website
Guest Editor
Department of Electrical and Biomedical Engineering, University of Nevada Reno, Reno, NV 89557, USA
Interests: biomimic robot design; snake robot control; shape morpihing mechanism
Special Issues, Collections and Topics in MDPI journals
Dr. Shan Luo

E-Mail Website
Guest Editor
Department of Engineering, King’s College London, London SE5 9NU, UK
Interests: visuo-tactile robotics; robot visuo-tactile sensing; multimodal robot perception; robot learning for grasping and manipulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The design and control of robots based on the biological mechanisms of plants and animals in nature is a key theme in robotics research. As a discipline arising from the fusion of new materials, mechanical design, motor control, and AI algorithms, biomimetics provides unique inspiration for advancing the development of cognitive and collaborative functionalities, as well as improving the dexterous and versatile manipulation capabilities of robots.

The aim of this second volume is to collect contributions on the design and control of bionic robots. By covering issues from biomimetic materials to biomimetic mechanical design, brain-inspired robotic cognition-related learning, and bioinspired control, our Special Issue provides an updated view of the status quo and perspectives in a rapidly growing field of the design and control of bionic robots. The present collection of papers, taking advantage of the open access format, is expected to provide a paradigm of the power of biomimetic approaches for discovering new important research avenues and for innovative solutions in the design and control of bionic robots.

To further its aims of combining basic research and applications, this Special Issue is divided into two main focuses:

  1. Design, covering topics such as: the mechanical design of biomimetic robots; intelligent sensors (e.g., vision, tactile, etc.) for perception and exploration; new materials; applications of biomimetic robotics in industry, e.g., manipulation, robot assisted surgery.
  2. Control, including bioinspired robotic learning and control, intelligent learning methods from a biomimetic view; computational neuroscience of perception and action; bionic motion control; advanced multi-modal sensing information fusion.

We believe that this initiative will fill an important gap in biomimetic technologies.

Prof. Dr. Peng Li
Prof. Dr. Yantao Shen
Dr. Shan Luo
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

  • mechanical design
  • biomimetic robots
  • intelligent sensors
  • new materials
  • bioinspired robotic learning and control
  • intelligent learning methods
  • computational neuroscience
  • advanced multimodal sensing information fusion

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

Published Papers (6 papers)

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Research

19 pages, 2646 KiB  
Article
Comparison of Empirical and Reinforcement Learning (RL)-Based Control Based on Proximal Policy Optimization (PPO) for Walking Assistance: Does AI Always Win?
by Nadine Drewing, Arjang Ahmadi, Xiaofeng Xiong and Maziar Ahmad Sharbafi
Biomimetics 2024, 9(11), 665; https://doi.org/10.3390/biomimetics9110665 - 1 Nov 2024
Viewed by 726
Abstract
The use of wearable assistive devices is growing in both industrial and medical fields. Combining human expertise and artificial intelligence (AI), e.g., in human-in-the-loop-optimization, is gaining popularity for adapting assistance to individuals. Amidst prevailing assertions that AI could surpass human capabilities in customizing [...] Read more.
The use of wearable assistive devices is growing in both industrial and medical fields. Combining human expertise and artificial intelligence (AI), e.g., in human-in-the-loop-optimization, is gaining popularity for adapting assistance to individuals. Amidst prevailing assertions that AI could surpass human capabilities in customizing every facet of support for human needs, our study serves as an initial step towards such claims within the context of human walking assistance. We investigated the efficacy of the Biarticular Thigh Exosuit, a device designed to aid human locomotion by mimicking the action of the hamstrings and rectus femoris muscles using Serial Elastic Actuators. Two control strategies were tested: an empirical controller based on human gait knowledge and empirical data and a control optimized using Reinforcement Learning (RL) on a neuromuscular model. The performance results of these controllers were assessed by comparing muscle activation in two assisted and two unassisted walking modes. Results showed that both controllers reduced hamstring muscle activation and improved the preferred walking speed, with the empirical controller also decreasing gastrocnemius muscle activity. However, the RL-based controller increased muscle activity in the vastus and rectus femoris, indicating that RL-based enhancements may not always improve assistance without solid empirical support. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Second Edition)
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18 pages, 16152 KiB  
Article
Characterization of Wing Kinematics by Decoupling Joint Movement in the Pigeon
by Yishi Shen, Shi Zhang, Weimin Huang, Chengrui Shang, Tao Sun and Qing Shi
Biomimetics 2024, 9(9), 555; https://doi.org/10.3390/biomimetics9090555 - 15 Sep 2024
Viewed by 861
Abstract
Birds have remarkable flight capabilities due to their adaptive wing morphology. However, studying live birds is time-consuming and laborious, and obtaining information about the complete wingbeat cycle is difficult. To address this issue and provide a complete dataset, we recorded comprehensive motion capture [...] Read more.
Birds have remarkable flight capabilities due to their adaptive wing morphology. However, studying live birds is time-consuming and laborious, and obtaining information about the complete wingbeat cycle is difficult. To address this issue and provide a complete dataset, we recorded comprehensive motion capture wing trajectory data from five free-flying pigeons (Columba livia). Five key motion parameters are used to quantitatively characterize wing kinematics: flapping, sweeping, twisting, folding and bending. In addition, the forelimb skeleton is mapped using an open-chain three-bar mechanism model. By systematically evaluating the relationship of joint degrees of freedom (DOFs), we configured the model as a 3-DOF shoulder, 1-DOF elbow and 2-DOF wrist. Based on the correlation analysis between wingbeat kinematics and joint movement, we found that the strongly correlated shoulder and wrist roll within the stroke plane cause wing flap and bending. There is also a strong correlation between shoulder, elbow and wrist yaw out of the stroke plane, which causes wing sweep and fold. By simplifying the wing morphing, we developed three flapping wing robots, each with different DOFs inside and outside the stroke plane. This study provides insight into the design of flapping wing robots capable of mimicking the 3D wing motion of pigeons. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Second Edition)
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15 pages, 5557 KiB  
Article
Stable Jumping Control Based on Deep Reinforcement Learning for a Locust-Inspired Robot
by Qijie Zhou, Gangyang Li, Rui Tang, Yi Xu, Hao Wen and Qing Shi
Biomimetics 2024, 9(9), 548; https://doi.org/10.3390/biomimetics9090548 - 11 Sep 2024
Viewed by 977
Abstract
Biologically inspired jumping robots exhibit exceptional movement capabilities and can quickly overcome obstacles. However, the stability and accuracy of jumping movements are significantly compromised by rapid changes in posture. Here, we propose a stable jumping control algorithm for a locust-inspired jumping robot based [...] Read more.
Biologically inspired jumping robots exhibit exceptional movement capabilities and can quickly overcome obstacles. However, the stability and accuracy of jumping movements are significantly compromised by rapid changes in posture. Here, we propose a stable jumping control algorithm for a locust-inspired jumping robot based on deep reinforcement learning. The algorithm utilizes a training framework comprising two neural network modules (actor network and critic network) to enhance training performance. The framework can control jumping by directly mapping the robot’s observations (robot position and velocity, obstacle position, target position, etc.) to its joint torques. The control policy increases randomness and exploration by introducing an entropy term to the policy function. Moreover, we designed a stage incentive mechanism to adjust the reward function dynamically, thereby improving the robot’s jumping stability and accuracy. We established a locus-inspired jumping robot platform and conducted a series of jumping experiments in simulation. The results indicate that the robot could perform smooth and non-flip jumps, with the error of the distance from the target remaining below 3%. The robot consumed 44.6% less energy to travel the same distance by jumping compared with walking. Additionally, the proposed algorithm exhibited a faster convergence rate and improved convergence effects compared with other classical algorithms. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Second Edition)
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18 pages, 12177 KiB  
Article
Multimodal Resonances of a Rectangular Planar Dielectric Elastomer Actuator and Its Application in a Robot with Soft Bristles
by Yangyang Du, Xiaojun Wu, Dan Wang, Futeng Zhao and Hua Hu
Biomimetics 2024, 9(8), 488; https://doi.org/10.3390/biomimetics9080488 - 13 Aug 2024
Viewed by 926
Abstract
Inspired by the fact that flying insects improve their power conversion efficiency through resonance, many soft robots driven by dielectric elastomer actuators (DEAs) have achieved optimal performance via first-order modal resonance. Besides first-order resonance, DEAs contribute to multiple innovative functions such as pumps [...] Read more.
Inspired by the fact that flying insects improve their power conversion efficiency through resonance, many soft robots driven by dielectric elastomer actuators (DEAs) have achieved optimal performance via first-order modal resonance. Besides first-order resonance, DEAs contribute to multiple innovative functions such as pumps that can make sounds when using multimodal resonances. This study presents the multimodal resonance of a rectangular planar DEA (RPDEA) with a central mass bias. Using a combination of experiments and finite element modeling (FEM), it was discerned that under a prestretch of 1.0 × 1.1, the first-, second-, and third-order resonances corresponded to vertical vibration, rotation along the long axis, and rotation along the short axis, respectively. In first-order resonance, superharmonic, harmonic, and subharmonic responses were activated, while only harmonic and subharmonic responses were observed in the second- and third-order resonances. Further investigations revealed that prestretching tended to inhibit third-order resonance but could elevate the resonance frequencies of the first and second orders. Conveniently, both the experimental and FEM results showed that the frequencies and amplitudes of the multimodal resonances could be tuned by adjusting the amplitudes of the excitation signals, referring to the direct current (DC) amplitude and alternating current (AC) amplitude, respectively. Moreover, instead of linear vibration, we found another novel approach that used rotation vibration to drive a robot with soft bristles via hopping locomotion, showcasing a higher speed compared to the first-order resonance in our robot. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Second Edition)
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16 pages, 2045 KiB  
Article
Design and Analysis of a Novel Bionic Tensegrity Robotic Fish with a Continuum Body
by Di Chen, Bo Wang, Yan Xiong, Jie Zhang, Ru Tong, Yan Meng and Junzhi Yu
Biomimetics 2024, 9(1), 19; https://doi.org/10.3390/biomimetics9010019 - 2 Jan 2024
Viewed by 2158
Abstract
Biological fish exhibit remarkable adaptability and exceptional swimming performance through their powerful and flexible bodies. Therefore, designing a continuum flexible body is significantly important for the development of a robotic fish. However, it is still challenging to replicate these functions of a biological [...] Read more.
Biological fish exhibit remarkable adaptability and exceptional swimming performance through their powerful and flexible bodies. Therefore, designing a continuum flexible body is significantly important for the development of a robotic fish. However, it is still challenging to replicate these functions of a biological body due to the limitations of actuation and material. In this paper, based on a tensegrity structure, we propose a bionic design scheme for a continuum robotic fish body with a property of stiffness variation. Its detailed structures and actuation principles are also presented. A mathematical model was established to analyze the bending characteristics of the tensegrity structure, which demonstrates the feasibility of mimicking the fish-like oscillation propulsion. Additionally, the stiffness variation mechanism is also exhibited experimentally to validate the effectiveness of the designed tensegrity fish body. Finally, a novel bionic robotic fish design scheme is proposed, integrating an electronic module-equipped fish head, a tensegrity body, and a flexible tail with a caudal fin. Subsequently, a prototype was developed. Extensive experiments were conducted to explore how control parameters and stiffness variation influence swimming velocity and turning performance. The obtained results reveal that the oscillation amplitude, frequency, and stiffness variation of the tensegrity robotic fish play crucial roles in swimming motions. With the stiffness variation, the developed tensegrity robotic fish achieves a maximum swimming velocity of 295 mm/s (0.84 body length per second, BL/s). Moreover, the bionic tensegrity robotic fish also performs a steering motion with a minimum turning radius of 230 mm (0.68 BL) and an angular velocity of 46.6°/s. The conducted studies will shed light on the novel design of a continuum robotic fish equipped with stiffness variation mechanisms. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Second Edition)
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17 pages, 16908 KiB  
Article
Modeling Contact Stiffness of Soft Fingertips for Grasping Applications
by Xiaolong Ma, Lingfeng Chen, Yanfeng Gao, Daliang Liu and Binrui Wang
Biomimetics 2023, 8(5), 398; https://doi.org/10.3390/biomimetics8050398 - 1 Sep 2023
Cited by 1 | Viewed by 1585
Abstract
Soft fingertips have distinct intrinsic features that allow robotic hands to offer adjustable and manageable stiffness for grasping. The stability of the grasp is determined by the contact stiffness between the soft fingertip and the object. Within this work, we proposed a line [...] Read more.
Soft fingertips have distinct intrinsic features that allow robotic hands to offer adjustable and manageable stiffness for grasping. The stability of the grasp is determined by the contact stiffness between the soft fingertip and the object. Within this work, we proposed a line vector representation method based on the Winkler Model and investigated the contact stiffness between soft fingertips and objects to achieve control over the gripping force and fingertip displacement of the gripper without the need for sensors integrated in the fingertip. First, we derived the stiffness matrix of the soft fingertip, analyzed the contact stiffness, and constructed the global stiffness matrix; then, we established the grasp stiffness matrix based on the contact stiffness model, allowing for the analysis and evaluation of the soft fingertip’s manipulating process. Finally, our experiment demonstrated that the variation in object orientation caused by external forces can indicate the contact force status between the fingertip and the object. This contact force status is determined by the contact stiffness. The position error between the theoretical work and tested data was less than 9%, and the angle error was less than 5.58%. The comparison between the theoretical contact stiffness and the experimental results at the interface indicate that the present model for the contact stiffness is appropriate and the theoretical contact stiffness is consistent with the experiment data. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Second Edition)
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