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Biomimetics
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Design and Control of a Bio-Inspired Robot
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Design and Control of a Bio-Inspired Robot

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 July 2023) | Viewed by 22969

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

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Published Papers (11 papers)

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Editorial

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3 pages, 148 KiB  
Editorial
Special Issue: Design and Control of a Bio-Inspired Robot
by Mingguo Zhao and Biao Hu
Biomimetics 2024, 9(1), 43; https://doi.org/10.3390/biomimetics9010043 - 10 Jan 2024
Cited by 1 | Viewed by 1532
Abstract
Bionics, the interdisciplinary field that draws inspiration from nature to design and develop innovative technologies, has paved the way for the creation of “bio-inspired robots” [...] Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)

Research

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20 pages, 37838 KiB  
Article
Research on Self-Stiffness Adjustment of Growth-Controllable Continuum Robot (GCCR) Based on Elastic Force Transmission
by Mingyuan Wang, Jianjun Yuan, Sheng Bao, Liang Du and Shugen Ma
Biomimetics 2023, 8(5), 433; https://doi.org/10.3390/biomimetics8050433 - 18 Sep 2023
Cited by 3 | Viewed by 1489
Abstract
Continuum robots have good adaptability in unstructured and complex environments. However, affected by their inherent nature of flexibility and slender structure, there are challenges in high-precision motion and load. Thus, stiffness adjustment for continuum robots has consistently attracted the attention of researchers. In [...] Read more.
Continuum robots have good adaptability in unstructured and complex environments. However, affected by their inherent nature of flexibility and slender structure, there are challenges in high-precision motion and load. Thus, stiffness adjustment for continuum robots has consistently attracted the attention of researchers. In this paper, a stiffness adjustment mechanism (SAM) is proposed and built in a growth-controllable continuum robot (GCCR) to improve the motion accuracy in variable scale motion. The self-stiffness adjustment is realized by antagonism through cable force transmission during the length change of the continuum robot. With a simple structure, the mechanism has a scarce impact on the weight and mass distribution of the robot and required no independent actuators for stiffness adjustment. Following this, a static model considering gravity and end load is established. The presented theoretical static model is applicable to predict the shape deformations of robots under different loads. The experimental validations showed that the maximum error ratio is within 5.65%. The stiffness of the robot can be enhanced by nearly 79.6%. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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16 pages, 3345 KiB  
Article
CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm
by Qingkai Li, Yanbo Pang, Yushi Wang, Xinyu Han, Qing Li and Mingguo Zhao
Biomimetics 2023, 8(5), 389; https://doi.org/10.3390/biomimetics8050389 - 25 Aug 2023
Cited by 4 | Viewed by 1906
Abstract
Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot’s operation. In this paper, inspired by [...] Read more.
Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot’s operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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16 pages, 3574 KiB  
Article
Applying an Artificial Neuromolecular System to the Application of Robotic Arm Motion Control in Assisting the Rehabilitation of Stroke Patients—An Artificial World Approach
by Jong-Chen Chen and Hao-Ming Cheng
Biomimetics 2023, 8(5), 385; https://doi.org/10.3390/biomimetics8050385 - 24 Aug 2023
Cited by 2 | Viewed by 1354
Abstract
Stroke patients cannot use their hands as freely as usual. However, recovery after a stroke is a long road for many patients. If artificial intelligence can assist human arm movement, it is believed that the possibility of stroke patients returning to normal hand [...] Read more.
Stroke patients cannot use their hands as freely as usual. However, recovery after a stroke is a long road for many patients. If artificial intelligence can assist human arm movement, it is believed that the possibility of stroke patients returning to normal hand movement can be significantly increased. In this study, the artificial neuromolecular system (ANM system) developed by our laboratory is used as the core motion control system to learn to control the mechanical arm, produce similar human rehabilitation actions, and assist patients in transiting between different activities. The strength of the ANM system lies in its ability to capture and process spatiotemporal information by exploiting the dynamic information processing inside neurons. Five experiments are conducted in this research: continuous learning, dimensionality reduction, moving problem domains, transfer learning, and fault tolerance. The results show that the ANM system can find out the arm movement trajectory when people perform different rehabilitation actions through the ability of continuous learning and reduce the activation of multiple muscle groups in stroke patients through the learning method of reducing dimensions. Finally, using the ANM system can reduce the learning time and performance required to switch between different actions through transfer learning. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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14 pages, 4106 KiB  
Article
A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG
by Yanbiao Li, Zhao Chen, Chentao Wu, Haoyu Mao and Peng Sun
Biomimetics 2023, 8(5), 382; https://doi.org/10.3390/biomimetics8050382 - 22 Aug 2023
Cited by 8 | Viewed by 2216
Abstract
In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control using simple reinforcement learning controllers particularly challenging. [...] Read more.
In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control using simple reinforcement learning controllers particularly challenging. This paper introduces a hierarchical reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve optimal motion control for quadruped robots. The framework consists of a high-level planner responsible for generating ideal motion parameters, a low-level controller using model predictive control (MPC), and a trajectory generator. The agents within the high-level planner are trained to provide the ideal motion parameters for the low-level controller. The low-level controller uses MPC and PD controllers to generate the foot-end force and calculates the joint motor torque through inverse kinematics. The simulation results show that the motion performance of the trained hierarchical framework is superior to that obtained using only the DDPG method. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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13 pages, 617 KiB  
Article
IDSNN: Towards High-Performance and Low-Latency SNN Training via Initialization and Distillation
by Xiongfei Fan, Hong Zhang and Yu Zhang
Biomimetics 2023, 8(4), 375; https://doi.org/10.3390/biomimetics8040375 - 18 Aug 2023
Cited by 3 | Viewed by 1610
Abstract
Spiking neural networks (SNNs) are widely recognized for their biomimetic and efficient computing features. They utilize spikes to encode and transmit information. Despite the many advantages of SNNs, they suffer from the problems of low accuracy and large inference latency, which are, respectively, [...] Read more.
Spiking neural networks (SNNs) are widely recognized for their biomimetic and efficient computing features. They utilize spikes to encode and transmit information. Despite the many advantages of SNNs, they suffer from the problems of low accuracy and large inference latency, which are, respectively, caused by the direct training and conversion from artificial neural network (ANN) training methods. Aiming to address these limitations, we propose a novel training pipeline (called IDSNN) based on parameter initialization and knowledge distillation, using ANN as a parameter source and teacher. IDSNN maximizes the knowledge extracted from ANNs and achieves competitive top-1 accuracy for CIFAR10 (94.22%) and CIFAR100 (75.41%) with low latency. More importantly, it can achieve 14× faster convergence speed than directly training SNNs under limited training resources, which demonstrates its practical value in applications. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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18 pages, 1234 KiB  
Article
Energy-Efficient Spiking Segmenter for Frame and Event-Based Images
by Hong Zhang, Xiongfei Fan and Yu Zhang
Biomimetics 2023, 8(4), 356; https://doi.org/10.3390/biomimetics8040356 - 10 Aug 2023
Cited by 8 | Viewed by 1916
Abstract
Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based cameras. However, there is currently no artificial neural network (ANN) that can [...] Read more.
Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based cameras. However, there is currently no artificial neural network (ANN) that can perform efficient segmentation on both types of images. This paper introduces spiking neural network (SNN, a bionic model that is energy-efficient when implemented on neuromorphic hardware) and develops a Spiking Context Guided Network (Spiking CGNet) with substantially lower energy consumption and comparable performance for both frame and event-based images. First, this paper proposes a spiking context guided block that can extract local features and context information with spike computations. On this basis, the directly-trained SCGNet-S and SCGNet-L are established for both frame and event-based images. Our method is verified on the frame-based dataset Cityscapes and the event-based dataset DDD17. On the Cityscapes dataset, SCGNet-S achieves comparable results to ANN CGNet with 4.85 × energy efficiency. On the DDD17 dataset, Spiking CGNet outperforms other spiking segmenters by a large margin. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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15 pages, 6185 KiB  
Article
Research on Walking Gait Planning and Simulation of a Novel Hybrid Biped Robot
by Peng Sun, Yunfei Gu, Haoyu Mao, Zhao Chen and Yanbiao Li
Biomimetics 2023, 8(2), 258; https://doi.org/10.3390/biomimetics8020258 - 15 Jun 2023
Cited by 7 | Viewed by 1936
Abstract
A kinematics analysis of a new hybrid mechanical leg suitable for bipedal robots was carried out and the gait of the robot walking on flat ground was planned. Firstly, the kinematics of the hybrid mechanical leg were analyzed and the applicable relevant models [...] Read more.
A kinematics analysis of a new hybrid mechanical leg suitable for bipedal robots was carried out and the gait of the robot walking on flat ground was planned. Firstly, the kinematics of the hybrid mechanical leg were analyzed and the applicable relevant models were established. Secondly, based on the preliminary motion requirements, the inverted pendulum model was used to divide the robot walking into three stages for gait planning: mid-step, start and stop. In the three stages of robot walking, the forward and lateral robot centroid motion trajectories and the swinging leg joint trajectories were calculated. Finally, dynamic simulation software was used to simulate the virtual prototype of the robot, achieving its stable walking on flat ground in the virtual environment, and verifying the feasibility of the mechanism design and gait planning. This study provides a reference for the gait planning of hybrid mechanical legged bipedal robots and lays the foundation for further research on the robots involved in this thesis. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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24 pages, 2306 KiB  
Article
SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems
by William R. P. Nourse, Clayton Jackson, Nicholas S. Szczecinski and Roger D. Quinn
Biomimetics 2023, 8(2), 247; https://doi.org/10.3390/biomimetics8020247 - 10 Jun 2023
Cited by 2 | Viewed by 1891
Abstract
One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is [...] Read more.
One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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15 pages, 1827 KiB  
Article
Online Running-Gait Generation for Bipedal Robots with Smooth State Switching and Accurate Speed Tracking
by Xiang Meng, Zhangguo Yu, Xuechao Chen, Zelin Huang, Chencheng Dong and Fei Meng
Biomimetics 2023, 8(1), 114; https://doi.org/10.3390/biomimetics8010114 - 10 Mar 2023
Cited by 6 | Viewed by 2635
Abstract
Smooth state switching and accurate speed tracking are important for the stability and reactivity of bipedal robots when running. However, previous studies have rarely been able to synthesize these two capabilities online. In this paper, we present an online running-gait generator for bipedal [...] Read more.
Smooth state switching and accurate speed tracking are important for the stability and reactivity of bipedal robots when running. However, previous studies have rarely been able to synthesize these two capabilities online. In this paper, we present an online running-gait generator for bipedal robots that allows for smooth state switching and accurate speed tracking. Considering a fluctuating height nature and computational expediency, the robot is represented by a simplified variable-height inverted-pendulum (VHIP) model. In order to achieve smooth state switching at the beginning and end of running, a segmented zero moment point (ZMP) trajectory optimization is proposed to automatically provide a feasible and smooth center-of-mass (CoM) trajectory that enables the robot to stably start or stop running at the given speed. To accurately track online the desired speed during running, we propose an iterative algorithm to compute target footholds, which allows for the robot to follow the interactive desired speed after the next two steps. Lastly, a numerical experiment and the simulation of online variable speed running were performed with position-controlled bipedal robot BHR7P, and the results verified the effectiveness of the proposed methods. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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Review

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39 pages, 8579 KiB  
Review
Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review
by Jianguo Wang, Shiwei Lin and Ang Liu
Biomimetics 2023, 8(4), 350; https://doi.org/10.3390/biomimetics8040350 - 7 Aug 2023
Cited by 4 | Viewed by 2896
Abstract
Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots [...] Read more.
Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people. Full article
(This article belongs to the Special Issue Design and Control of a Bio-Inspired Robot)
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