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Biomimetics
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Design and Control of a Bio-Inspired Robot: 3rd Edition
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Design and Control of a Bio-Inspired Robot: 3rd Edition

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 7877

Special Issue Editors

Prof. Dr. Mingguo Zhao

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: legged locomotion; whole-body control; neuromorphic computing; humanoid robots
Special Issues, Collections and Topics in MDPI journals
Dr. Biao Hu

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: multi-robot path planning; robot perception; cloud robot system; brain-inspired computing system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A “Bionic robot” simulates related biological mechanisms to achieve specific functions. Bionics is mainly embodied in robot structure design, perception, control, and decision-making methods. A biomimetic robot has inherent advantages in some performance aspects, which is important in the robot research field and useful in many application scenarios. 

In recent years, with the development of physiology and brain science, many new achievements can be applied to robotics, This includes the imitation of organisms by robots in terms of structure and materials, the reference of biological mechanisms of perception systems such as vision and touch, and biological systems’ positioning. In addition, the above involve simulating the brain nervous system’s higher-level cognition and intelligence in learning, reasoning, memory, and emotion, which could lead to major changes in robots’ intelligence.

This Special Issue calls for the latest research results on bionic design and algorithms of robot motion, sensing, and positioning systems.

Prof. Dr. Mingguo Zhao
Dr. Biao Hu
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

  • biomimetic design of robot hand, arm, leg, foot, head, etc.
  • bionic vision, SLAM, and locomotion
  • bionic cognitive and decision making
  • brain-like computing
  • neuromorphic system and neuromorphic computing

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Published Papers (5 papers)

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Research

24 pages, 19716 KiB  
Article
Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots
by Tianbo Yang, Yuchuang Tong and Zhengtao Zhang
Biomimetics 2025, 10(1), 30; https://doi.org/10.3390/biomimetics10010030 - 6 Jan 2025
Viewed by 995
Abstract
With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are straightforward [...] Read more.
With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model’s inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 3rd Edition)
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22 pages, 9626 KiB  
Article
Design of an Active–Passive Composite Impedance Controller for a Soft Robotic Arm Under Contact Constraints
by Bo Yan, Yinglong Chen, Cheng Zhou, Qiang Sun, Fei Gao, Xinyu Yang and Xingtian Xiao
Biomimetics 2024, 9(12), 742; https://doi.org/10.3390/biomimetics9120742 - 5 Dec 2024
Viewed by 1087
Abstract
The inherent passive impedance characteristics of soft robotic arms provide excellent environmental adaptability. When a soft robotic arm interacts with its surroundings, its passive impedance responds swiftly, preventing rigid collisions that could damage the arm and ensuring high safety. However, during the movement [...] Read more.
The inherent passive impedance characteristics of soft robotic arms provide excellent environmental adaptability. When a soft robotic arm interacts with its surroundings, its passive impedance responds swiftly, preventing rigid collisions that could damage the arm and ensuring high safety. However, during the movement of the soft robotic arm, these passive impedance properties are uncontrollable, making it impossible to achieve precise impedance control in constrained environments by relying solely on passive mechanisms. Therefore, this paper integrated active impedance control with the passive impedance characteristics of soft robotic arms, proposing an active–passive composite impedance controller. Additionally, a position-based impedance controller was designed for comparative analysis. Finally, this article developed both control systems and conducted simulations and experiments, demonstrating that the composite active–passive impedance controller offers superior control performance and environmental adaptability. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 3rd Edition)
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15 pages, 6968 KiB  
Article
Biomimetic Active Stereo Camera System with Variable FOV
by Yanmiao Zhou and Xin Wang
Biomimetics 2024, 9(12), 740; https://doi.org/10.3390/biomimetics9120740 - 4 Dec 2024
Viewed by 1086
Abstract
Inspired by the biological eye movements of fish such as pipefish and sandlances, this paper presents a novel dynamic calibration method specifically for active stereo vision systems to address the challenges of active cameras with varying fields of view (FOVs). By integrating static [...] Read more.
Inspired by the biological eye movements of fish such as pipefish and sandlances, this paper presents a novel dynamic calibration method specifically for active stereo vision systems to address the challenges of active cameras with varying fields of view (FOVs). By integrating static calibration based on camera rotation angles with dynamic updates of extrinsic parameters, the method leverages relative pose adjustments between the rotation axis and cameras to update extrinsic parameters continuously in real-time. It facilitates epipolar rectification as the FOV changes, and enables precise disparity computation and accurate depth information acquisition. Based on the dynamic calibration method, we develop a two-DOF bionic active camera system including two cameras driven by motors to mimic the movement of biological eyes; this compact system has a large range of visual data. Experimental results show that the calibration method is effective, and achieves high accuracy in extrinsic parameter calculations during FOV adjustments. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 3rd Edition)
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16 pages, 5339 KiB  
Article
Stair-Climbing Wheeled Robot Based on Rotating Locomotion of Curved-Spoke Legs
by Dongwoo Seo and Jaeyoung Kang
Biomimetics 2024, 9(10), 633; https://doi.org/10.3390/biomimetics9100633 - 17 Oct 2024
Cited by 1 | Viewed by 2118
Abstract
This study proposes a new wheel-leg mechanism concept and formulations for the kinematics and dynamics of a stair-climbing robot utilizing the rotating leg locomotion of curved spokes and rolling tires. The system consists of four motor-driven tires and four curved-spoke legs. The curved-spoke [...] Read more.
This study proposes a new wheel-leg mechanism concept and formulations for the kinematics and dynamics of a stair-climbing robot utilizing the rotating leg locomotion of curved spokes and rolling tires. The system consists of four motor-driven tires and four curved-spoke legs. The curved-spoke leg is semicircle-like and is used to climb stairs. Once the spoke leg rolls on the surface, it lifts and pulls the mating wheel toward the surface, owing to the kinematic constraint between the spoke and the wheel. Single-wheel climbing is a necessary condition for the stair climbing of whole robots equipped with front and rear axles. This study proposes the design requirements of a spoke leg for the success of single-wheel climbing in terms of kinematic inequality equations according to the scenario of single-wheel climbing. For a design configuration that enables single-wheel climbing, the required minimum friction coefficient for the static analysis of the stair-climbing wheeled robots is demon-strated. Thereafter, the stair-climbing ability is validated through the dynamic equations that enable the frictional slip of the tires, as well as the curved-spoke legs. Lastly, the results revealed that the rotating locomotion of the well-designed curved-spoke legs effectively enables the stair climbing of the whole robot. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 3rd Edition)
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25 pages, 5281 KiB  
Article
A Bio-Inspired Dopamine Model for Robots with Autonomous Decision-Making
by Marcos Maroto-Gómez, Javier Burguete-Alventosa, Sofía Álvarez-Arias, María Malfaz and Miguel Ángel Salichs
Biomimetics 2024, 9(8), 504; https://doi.org/10.3390/biomimetics9080504 - 21 Aug 2024
Cited by 1 | Viewed by 1944
Abstract
Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating [...] Read more.
Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model’s performance in five scenarios, emphasising how dopamine levels vary depending on the robot’s situation and stimuli perception. Moreover, we show the model’s integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 3rd Edition)
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