Editorial

3 pages, 148 KiB

Open AccessEditorial

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

Viewed by 1015

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

Open AccessArticle

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 2 | Viewed by 1030

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

Open AccessArticle

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 2 | Viewed by 1089

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

Open AccessArticle

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 1 | Viewed by 740

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

Open AccessArticle

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 3 | Viewed by 1299

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

Open AccessArticle

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 2 | Viewed by 1067

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 \times

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

Open AccessArticle

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 4 | Viewed by 1343

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

Open AccessArticle

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 6 | Viewed by 1268

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

Open AccessArticle

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 1 | Viewed by 1231

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

Open AccessArticle

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 4 | Viewed by 1898

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

Open AccessReview

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 - 07 Aug 2023

Cited by 2 | Viewed by 1833

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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