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Biomimetics
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Design and Control of a Bio-Inspired Robot: 2nd Edition
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Design and Control of a Bio-Inspired Robot: 2nd 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 (15 March 2024) | Viewed by 4729

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. The biomimetic robot has inherent advantages in some aspects of performance, which is important in the field of robot research and will be 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 and the reference of biological mechanisms of perception systems, such as vision, touch, and the positioning of biological systems. In addition, it involves simulating the higher-level cognition and intelligence of the brain's nervous system in learning, reasoning, memory, and emotion. All of this could lead to major changes in the intelligence of robots.

This Special Issue calls for the latest research results of bionic design and bionic 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
  • neuromophic system and neuromophic computing

Published Papers (4 papers)

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Research

19 pages, 3181 KiB  
Article
Humanoid Head Camera Stabilization Using a Soft Robotic Neck and a Robust Fractional Order Controller
by Jorge Muñoz, Raúl de Santos-Rico, Lisbeth Mena and Concepción A. Monje
Biomimetics 2024, 9(4), 219; https://doi.org/10.3390/biomimetics9040219 - 07 Apr 2024
Viewed by 575
Abstract
In this paper, a new approach for head camera stabilization of a humanoid robot head is proposed, based on a bio-inspired soft neck. During walking, the sensors located on the humanoid’s head (cameras or inertial measurement units) show disturbances caused by the torso [...] Read more.
In this paper, a new approach for head camera stabilization of a humanoid robot head is proposed, based on a bio-inspired soft neck. During walking, the sensors located on the humanoid’s head (cameras or inertial measurement units) show disturbances caused by the torso inclination changes inherent to this process. This is currently solved by a software correction of the measurement, or by a mechanical correction by motion cancellation. Instead, we propose a novel mechanical correction, based on strategies observed in different animals, by means of a soft neck, which is used to provide more natural and compliant head movements. Since the neck presents a complex kinematic model and nonlinear behavior due to its soft nature, the approach requires a robust control solution. Two different control approaches are addressed: a classical PID controller and a fractional order controller. For the validation of the control approaches, an extensive set of experiments is performed, including real movements of the humanoid, different head loading conditions or transient disturbances. The results show the superiority of the fractional order control approach, which provides higher robustness and performance. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 2nd Edition)
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19 pages, 5229 KiB  
Article
Robot Arm Reaching Based on Inner Rehearsal
by Jiawen Wang, Yudi Zou, Yaoyao Wei, Mengxi Nie, Tianlin Liu and Dingsheng Luo
Biomimetics 2023, 8(6), 491; https://doi.org/10.3390/biomimetics8060491 - 18 Oct 2023
Viewed by 1613
Abstract
Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of [...] Read more.
Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot’s ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 2nd Edition)
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17 pages, 2833 KiB  
Article
Whole-Body Dynamics-Based Aerial Fall Trajectory Optimization and Landing Control for Humanoid Robot
by Weilong Zuo, Junyao Gao, Jingwei Cao, Xilong Xin, Mingyue Jin and Xuechao Chen
Biomimetics 2023, 8(6), 460; https://doi.org/10.3390/biomimetics8060460 - 01 Oct 2023
Viewed by 1056
Abstract
When humanoid robots work in human environments, falls are inevitable due to the complexity of such environments. Current research on humanoid robot falls has mainly focused on falls on the ground, with little research on humanoid robots falling from the air. In this [...] Read more.
When humanoid robots work in human environments, falls are inevitable due to the complexity of such environments. Current research on humanoid robot falls has mainly focused on falls on the ground, with little research on humanoid robots falling from the air. In this paper, we employ an extended state variable formulation that directly maps from the high-level motion strategy space to the full-body joint space to optimize the falling trajectory in order to protect the robot when falling from the air. In order to mitigate the impact force generated by the robot’s fall, during the aerial phase, we employ simple proportion differentiation (PD) control. In the landing phase, we optimize the optimal contact force at the contact point using the centroidal dynamics model. Based on the contact force, the changes to the end-effector positions are solved using a dual spring–damper model. In the simulation experiments, we conduct three comparative experiments, and the simulation results demonstrate that the robot can safely fall 1.5 m from the ground at a pitch angle of 45°. Finally, we experimentally validate the methods on an actual robot by performing a side-fall experiment. The experimental results show that the proposed trajectory optimization and motion control methods can provide excellent shock absorption for the impact generated when a robot falls. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 2nd Edition)
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17 pages, 11891 KiB  
Article
LFVB-BioSLAM: A Bionic SLAM System with a Light-Weight LiDAR Front End and a Bio-Inspired Visual Back End
by Ruilan Gao, Zeyu Wan, Sitong Guo, Changjian Jiang and Yu Zhang
Biomimetics 2023, 8(5), 410; https://doi.org/10.3390/biomimetics8050410 - 05 Sep 2023
Cited by 1 | Viewed by 1043
Abstract
Simultaneous localization and mapping (SLAM) is one of the crucial techniques applied in autonomous robot navigation. The majority of present popular SLAM algorithms are built within probabilistic optimization frameworks, achieving high accuracy performance at the expense of high power consumption and latency. In [...] Read more.
Simultaneous localization and mapping (SLAM) is one of the crucial techniques applied in autonomous robot navigation. The majority of present popular SLAM algorithms are built within probabilistic optimization frameworks, achieving high accuracy performance at the expense of high power consumption and latency. In contrast to robots, animals are born with the capability to efficiently and robustly navigate in nature, and bionic SLAM algorithms have received increasing attention recently. Current bionic SLAM algorithms, including RatSLAM, with relatively low accuracy and robustness, tend to fail in certain challenging environments. In order to design a bionic SLAM system with a novel framework and relatively high practicality, and to facilitate the development of bionic SLAM research, in this paper we present LFVB-BioSLAM, a bionic SLAM system with a light-weight LiDAR-based front end and a bio-inspired vision-based back end. We adopt a range flow-based LiDAR odometry as the front end of the SLAM system, providing the odometry estimation for the back end, and we propose a biologically-inspired back end processing algorithm based on the monocular RGB camera, performing loop closure detection and path integration. Our method is verified through real-world experiments, and the results show that LFVB-BioSLAM outperforms RatSLAM, a vision-based bionic SLAM algorithm, and RF2O, a laser-based horizontal planar odometry algorithm, in terms of accuracy and robustness. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot: 2nd Edition)
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