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Biomimetics
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Biology for Robotics and Robotics for Biology
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Biology for Robotics and Robotics for Biology

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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 10209

Special Issue Editor

Dr. Liang Li

E-Mail Website
Guest Editor
Max Planck Institute of Animal Behavior, D-78467 Konstanz, Germany
Interests: bioinspired mechanical design; bioinspired control; bioinspired perception; bioinspired locomotion; bioinspired behavior; bioinspired swarm

Special Issue Information

Dear Colleagues,

Engineers have long drawn inspiration from biological studies, resulting in remarkable advancements in bioinspired robotics. This has led to the development of robots such as RoboTuna, which is fish-inspired; RoboBees, which is insect-inspired; and the more recent BigDog, which is dog-inspired. However, the potential of these robots in fundamental biological research has received comparatively less attention. Fortunately, recent studies have increasingly focused on the application of bioinspired robots for such purposes. Furthermore, a deeper knowledge of fundamental biological studies can also enhance the development of bioinspired robots. Considering the tightly coupled relationship between the two directions and their coevolving properties, we believe that it is time to create a Special Issue that brings together the latest developments from both areas.

This Special Issue aims to establish a close connection between the fields of biology and robotics, encompassing both biology-inspired robotics and robotics-inspired biology. The theme is highly interdisciplinary and covers a wide range of topics in both biology and robotics, such as bioinspired mechanical design, bioinspired control, bioinspired perception, bioinspired locomotion, bioinspired behavior, and bioinspired swarm. It also includes studies on robot-inspired locomotion, robot-inspired behavior, and robot-inspired neurosystems. The scope is not limited to these areas and extends to other relevant topics as well.

Dr. Liang Li
Guest Editor

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

  • bioinspired mechanical design
  • bioinspired control
  • bioinspired perception
  • bioinspired locomotion
  • bioinspired behavior
  • bioinspired swarm
  • studies on robot-inspired locomotion
  • robot-inspired behavior
  • robot-inspired neurosystems

Published Papers (7 papers)

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Research

18 pages, 58113 KiB  
Article
Bioinspired Design and Experimental Validation of an Aquatic Snake Robot
by Giovanni Bianchi, Luca Lanzetti, Daniele Mariana and Simone Cinquemani
Biomimetics 2024, 9(2), 87; https://doi.org/10.3390/biomimetics9020087 - 1 Feb 2024
Viewed by 1196
Abstract
This article presents the design, simulation, and experimental validation of a novel modular aquatic snake robot capable of surface locomotion. The modular structure allows each unit to function independently, facilitating ease of maintenance and adaptability to diverse aquatic environments. Employing the material point [...] Read more.
This article presents the design, simulation, and experimental validation of a novel modular aquatic snake robot capable of surface locomotion. The modular structure allows each unit to function independently, facilitating ease of maintenance and adaptability to diverse aquatic environments. Employing the material point method with the moving least squares (MPM-MLS) simulation technique, the robot’s dynamic behavior was analyzed, yielding reliable results. The control algorithm, integral to the robot’s autonomous navigation, was implemented to enable forward propulsion at high speed, steering, and obstacle detection and avoidance. Extensive testing of the aquatic snake robot was conducted, demonstrating its practical viability. The robot showcased promising swimming capabilities, achieving high speeds and maneuverability. Furthermore, the obstacle detection and avoidance mechanisms were proven effective, showing the robot’s ability to navigate through dynamic environments. The presented aquatic snake robot represents an advancement in the field of underwater robotics, offering a modular and versatile solution for tasks ranging from environmental monitoring to search and rescue operations. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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18 pages, 739 KiB  
Article
Online Inverse Optimal Control for Time-Varying Cost Weights
by Sheng Cao, Zhiwei Luo and Changqin Quan
Biomimetics 2024, 9(2), 84; https://doi.org/10.3390/biomimetics9020084 - 31 Jan 2024
Viewed by 992
Abstract
Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown [...] Read more.
Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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24 pages, 7365 KiB  
Article
Running Gait and Control of Quadruped Robot Based on SLIP Model
by Xiaolong He, Xinjie Li, Xiangji Wang, Fantuo Meng, Xikang Guan, Zhenyu Jiang, Lipeng Yuan, Kaixian Ba, Guoliang Ma and Bin Yu
Biomimetics 2024, 9(1), 24; https://doi.org/10.3390/biomimetics9010024 - 3 Jan 2024
Viewed by 1327
Abstract
Legged robots have shown great adaptability to various environments. However, conventional walking gaits are insufficient to meet the motion requirements of robots. Therefore, achieving high-speed running for legged robots has become a significant research topic. In this paper, based on the Spring-Loaded Inverted [...] Read more.
Legged robots have shown great adaptability to various environments. However, conventional walking gaits are insufficient to meet the motion requirements of robots. Therefore, achieving high-speed running for legged robots has become a significant research topic. In this paper, based on the Spring-Loaded Inverted Pendulum (SLIP) model and the optimized Double leg—Spring-Loaded Inverted Pendulum (D-SLIP) model, the running control strategies for the double flying phase Bound gait and the Rotatory gallop gait of quadruped robots are designed. First, the dynamics of the double flying phase Bound gait and Rotatory gallop gait are analyzed. Then, based on the “three-way” control idea of the SLIP model, the running control strategy for the double flying phase Bound gait is designed. Subsequently, the SLIP model is optimized to derive the D-SLIP model with two touchdown legs, and its dynamic characteristics are analyzed. And the D-SLIP model is applied to the running control strategy of the Rotatory gallop gait. Furthermore, joint simulation verification is conducted using Adams virtual prototyping and MATLAB/Simulink control systems for the designed control strategies. Finally, experimental verification is performed for the double flying phase Bound gait running control strategy. The experimental results demonstrate that the quadruped robot can achieve high-speed and stable running. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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21 pages, 8706 KiB  
Article
Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics
by Xuelong Sun, Cheng Hu, Tian Liu, Shigang Yue, Jigen Peng and Qinbing Fu
Biomimetics 2023, 8(8), 580; https://doi.org/10.3390/biomimetics8080580 - 1 Dec 2023
Viewed by 1182
Abstract
Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability [...] Read more.
Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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26 pages, 7041 KiB  
Article
Derivation of Ultra-High Gain Hybrid Converter Families for HASEL Actuators Used in Soft Mobile Robots
by Tirthasarathi Lodh and Hanh-Phuc Le
Biomimetics 2023, 8(6), 483; https://doi.org/10.3390/biomimetics8060483 - 12 Oct 2023
Viewed by 1287
Abstract
This work proposes, analyzes, designs, and validates superior topologies of UHGH converters that are capable of supporting extremely large conversion ratios up to ∼2000× and output voltage up to ∼4–12 kV for future mobile soft robots from an input voltage as low as [...] Read more.
This work proposes, analyzes, designs, and validates superior topologies of UHGH converters that are capable of supporting extremely large conversion ratios up to ∼2000× and output voltage up to ∼4–12 kV for future mobile soft robots from an input voltage as low as the range of a 1-cell battery pack. Thus, the converter makes soft robots standalone systems that can be untethered and mobile. The extremely large voltage gain is enabled by a unique hybrid combination of a high-gain switched magnetic element (HGSME) and a capacitor-based voltage multiplier rectifier (CVMR) that, together, achieve small overall size, efficient operation, and output voltage regulation and shaping with simple duty-cycle modulation. With superior performance, power density, and compact size, the UHGH converters prove to be a promising candidate for future untethered soft robots. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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23 pages, 10973 KiB  
Article
Bioinspired Rigid–Flexible Coupled Adaptive Compliant Motion Control of Robot Gecko for Space Stations
by Xiangli Pei, Shuhao Liu, Anmin Wei, Ruizhuo Shi and Zhendong Dai
Biomimetics 2023, 8(5), 415; https://doi.org/10.3390/biomimetics8050415 - 6 Sep 2023
Cited by 1 | Viewed by 1334
Abstract
This paper presents a study on bioinspired rigid-flexible coupling adaptive compliant motion control of a robot gecko with hybrid actuation for space stations. The biomimetic robot gecko is made of a rigid trunk, four motor-driven active legs with dual-degree-of-freedom shoulder joints, and four [...] Read more.
This paper presents a study on bioinspired rigid-flexible coupling adaptive compliant motion control of a robot gecko with hybrid actuation for space stations. The biomimetic robot gecko is made of a rigid trunk, four motor-driven active legs with dual-degree-of-freedom shoulder joints, and four pneumatic flexible pleated active attachment–detachment feet. The adaptive impedance model consists of four input parameters: the inertia coefficient, stiffness coefficient, damping coefficient, and segmented expected plantar force. The robot gecko is equipped with four force sensors mounted on its four feet, from which the normal force of each foot can be sensed in real-time. Based on the sensor signal, the variable stiffness characteristics of the feet in different states are analyzed. Furthermore, an adaptive active compliance control strategy with whole-body rigidity–flexibility-force feedback coupling is proposed for the robot gecko. Four sets of experiments are presented, including open-loop motion control, static anti-interference experiment, segmented variable stiffness experiment, and adaptative compliant motion control, both in a microgravity environment. The experiment results indicated that the presented control strategy worked well and the robot gecko demonstrates the capability of stable attachment and compliant detachment, thereby normal impact and microgravity instability are avoided. It achieves position tracking and force tracking while exhibiting strong robustness for external disturbances. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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12 pages, 2073 KiB  
Article
Bridging Locomotion and Manipulation Using Reconfigurable Robotic Limbs via Reinforcement Learning
by Haoran Sun, Linhan Yang, Yuping Gu, Jia Pan, Fang Wan and Chaoyang Song
Biomimetics 2023, 8(4), 364; https://doi.org/10.3390/biomimetics8040364 - 14 Aug 2023
Cited by 2 | Viewed by 2253
Abstract
Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains [...] Read more.
Locomotion and manipulation are two essential skills in robotics but are often divided or decoupled into two separate problems. It is widely accepted that the topological duality between multi-legged locomotion and multi-fingered manipulation shares an intrinsic model. However, a lack of research remains to identify the data-driven evidence for further research. This paper explores a unified formulation of the loco-manipulation problem using reinforcement learning (RL) by reconfiguring robotic limbs with an overconstrained design into multi-legged and multi-fingered robots. Such design reconfiguration allows for adopting a co-training architecture for reinforcement learning towards a unified loco-manipulation policy. As a result, we find data-driven evidence to support the transferability between locomotion and manipulation skills using a single RL policy with a multilayer perceptron or graph neural network. We also demonstrate the Sim2Real transfer of the learned loco-manipulation skills in a robotic prototype. This work expands the knowledge frontiers on loco-manipulation transferability with learning-based evidence applied in a novel platform with overconstrained robotic limbs. Full article
(This article belongs to the Special Issue Biology for Robotics and Robotics for Biology)
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