Biologically Inspired Design and Control of Robots: Third Edition

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

Deadline for manuscript submissions: 20 April 2026 | Viewed by 713

Special Issue Editors


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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

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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

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 in advancing the development of cognitive and collaborative functionalities, as well as improving the dexterous and versatile manipulation capabilities of robots.

The aim of this Special Issue 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 up-to-date overview of the current status quo and future perspectives in the rapidly growing field of bionic robot  design and control. By leveraging the open access format, this collection of papers aims to exemplify the potential of biomimetic approaches to uncover novel research pathways and inspire innovative solutions in the design and control of bionic robots.

To advance its goals of bridging basic research and applications, this Special Issue is organized around two main areas of focus:

  • (a) Design, covering topics such as mechanical design of biomimetic robots; intelligent sensors (e.g., vision, tactile etc.) for perception and exploration; new materials; and industrial applications of biomimetic robotics, including manipulation and robot-assisted surgery.
  • (b) Control, including bioinspired learning and control of robots; intelligent learning methods from a biomimetic perspective; computational neuroscience of perception and action; bionic motion control; advanced multimodal sensory information fusion.

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

Prof. Dr. Peng Li
Prof. Dr. Yantao Shen
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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (1 paper)

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Research

18 pages, 9273 KB  
Article
Explosive Output to Enhance Jumping Ability: A Variable Reduction Ratio Design Paradigm for Humanoid Robot Knee Joint
by Xiaoshuai Ma, Qingqing Li, Haochen Xu, Xuechao Chen, Junyao Gao and Fei Meng
Biomimetics 2026, 11(1), 45; https://doi.org/10.3390/biomimetics11010045 - 6 Jan 2026
Viewed by 498
Abstract
Enhancing the explosive power output of the knee joints is critical for improving the agility and obstacle crossing of humanoid robots. However, a mismatch between the knee-to-CoM transmission ratio and jumping demands, together with power-loss–induced motor performance degradation at high speeds, shortens the [...] Read more.
Enhancing the explosive power output of the knee joints is critical for improving the agility and obstacle crossing of humanoid robots. However, a mismatch between the knee-to-CoM transmission ratio and jumping demands, together with power-loss–induced motor performance degradation at high speeds, shortens the high-power operating window and limits jump performance. To address this, this paper introduces a variable-reduction-ratio knee-joint paradigm in which the reduction ratio is coupled to the joint angle and decreases during extension. Analysis of motor output and knee kinematics motivates coupling the reduction ratio to the joint angle. A high initial ratio increases the takeoff torque, and a gradual decrease limits motor speed and power losses, extending the high-power window. A linear-actuator-driven guide-rod mechanism realizes this strategy, and parameter optimization guided by explosive jump control is employed to select the design parameters. Experimental validation demonstrates a high jump of 0.63 m on a single-joint platform (a theoretical improvement of 31.9% over the optimal fixed-ratio baseline under the tested conditions). Integrated into a humanoid robot, the proposed design enables a 1.1 m long jump, a 0.5 m high jump, and a 0.5 m box jump. Full article
(This article belongs to the Special Issue Biologically Inspired Design and Control of Robots: Third Edition)
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