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Biomimetics
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Computer-Aided Biomimetics: 2nd Edition
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Computer-Aided Biomimetics: 2nd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6095

Special Issue Editors

Prof. Dr. Yong Peng

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: bionic structure design; traffic and vehicle crash safety; simulation; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Danqi Wang

E-Mail Website
Guest Editor
School of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China
Interests: conceptual design; structural safety and energy saving design; multidisciplinary optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Dongkai Chu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan, China
Interests: bionic structure design, optimization, and processing
Special Issues, Collections and Topics in MDPI journals
Dr. Honghao Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan, China
Interests: bionic structure design; biomaterials; numerical simulation; multidisciplinary optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer-aided biomimetics is an interdisciplinary research field that combines computer science and biomimetics. Drawing inspiration from nature's excellent designs, computer-aided biomimetics utilizes computer modeling and simulation techniques to mimic biological systems and apply the derived design and optimization insights to engineering and scientific fields. It finds wide-ranging applications and potential in areas such as materials science, mechanical engineering, aerospace, medicine, energy, and many more. The emergence of computer-aided biomimetics opens up new possibilities for engineers and scientists to tackle real-world problems, and holds the potential to drive technological innovation and scientific progress in the future. Many advanced technologies have been applied to the field of computer-aided biomimetics.

The purpose of this Special Issue is to incorporate the latest research studies in the field of advanced methods and applications, from either theoretical or practical perspectives. The relevant topics for this Special Issue include but are not limited to the following areas:

  • Multi-scale modeling and design of material structures;
  • Conceptual design and bio-structure design;
  • Bionic functional surface and bionic structure processing;
  • Mechanical structure and motion control of robots based on the bionics principle;
  • Performance analysis and evaluation of computer-aided biomimetics;
  • Multidisciplinary optimization algorithms for computer-aided biomimetics;
  • Application of AI in computer-aided biomimetics;
  • Other related research topics.

Dr. Honghao Zhang
Prof. Dr. Yong Peng
Dr. Danqi Wang
Dr. Dongkai Chu
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

  • computer modeling
  • numerical simulation
  • bionic structures
  • design and optimization
  • motion control
  • bionic functional surface
  • performance analysis

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Related Special Issue

Published Papers (7 papers)

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Research

25 pages, 28881 KiB  
Article
Antagonistic Feedback Control of Muscle Length Changes for Efficient Involuntary Posture Stabilization
by Masami Iwamoto, Noritoshi Atsumi and Daichi Kato
Biomimetics 2024, 9(10), 618; https://doi.org/10.3390/biomimetics9100618 (registering DOI) - 11 Oct 2024
Viewed by 384
Abstract
Simultaneous and cooperative muscle activation results in involuntary posture stabilization in vertebrates. However, the mechanism through which more muscles than joints contribute to this stabilization remains unclear. We developed a computational human body model with 949 muscle action lines and 22 joints and [...] Read more.
Simultaneous and cooperative muscle activation results in involuntary posture stabilization in vertebrates. However, the mechanism through which more muscles than joints contribute to this stabilization remains unclear. We developed a computational human body model with 949 muscle action lines and 22 joints and examined muscle activation patterns for stabilizing right upper or lower extremity motions at a neutral body posture (NBP) under gravity using actor—critic reinforcement learning (ACRL). Two feedback control models (FCM), muscle length change (FCM—ML) and joint angle differences, were applied to ACRL with a normalized Gaussian network (ACRL—NGN) or deep deterministic policy gradient. Our findings indicate that among the six control methods, ACRL—NGN with FCM—ML, utilizing solely antagonistic feedback control of muscle length change without relying on synergy pattern control or categorizing muscles as flexors, extensors, agonists, or synergists, achieved the most efficient involuntary NBP stabilization. This finding suggests that vertebrate muscles are fundamentally controlled without categorization of muscles for targeted joint motion and are involuntarily controlled to achieve the NBP, which is the most comfortable posture under gravity. Thus, ACRL—NGN with FCM—ML is suitable for controlling humanoid muscles and enables the development of a comfortable seat design. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
22 pages, 10074 KiB  
Article
Impact of Vehicle Steering Strategy on the Severity of Pedestrian Head Injury
by Danqi Wang, Wengang Deng, Lintao Wu, Li Xin, Lizhe Xie and Honghao Zhang
Biomimetics 2024, 9(10), 593; https://doi.org/10.3390/biomimetics9100593 - 30 Sep 2024
Viewed by 495
Abstract
In response to the sudden violation of pedestrians crossing the road, intelligent vehicles take into account factors such as the road conditions in the accident zone, traffic rules, and surrounding vehicles’ driving status to make emergency evasive decisions. Thus, the collision simulation models [...] Read more.
In response to the sudden violation of pedestrians crossing the road, intelligent vehicles take into account factors such as the road conditions in the accident zone, traffic rules, and surrounding vehicles’ driving status to make emergency evasive decisions. Thus, the collision simulation models for pedestrians and three types of vehicles, i.e., sedans, Sport Utility Vehicles (SUVs), and Multi-Purpose Vehicle (MPVs), are built to investigate the impact of vehicle types, vehicle steering angles, collision speeds, collision positions, and pedestrian orientations on head injuries of pedestrians. The results indicate that the Head Injury Criterion (HIC) value of the head increases with the increase in collision speed. Regarding the steering angles, when a vehicle’s steering direction aligns with a pedestrian’s position, the pedestrian remains on top of the vehicle’s hood for a longer period and moves together with the vehicle after the collision. This effectively reduces head injuries to pedestrians. However, when the vehicle’s steering direction is opposite to the pedestrian’s position, the pedestrian directly collides with the ground, resulting in higher head injuries. Among them, MPVs cause the most severe injuries, followed by SUVs, and sedans have the least impact. Overall, intelligent vehicles have great potential to reduce head injuries of pedestrians in the event of sudden pedestrian-vehicle collisions by combining with Automatic Emergency Steering (AES) measures. In the future, efforts need to be made to establish an optimized steering strategy and optimize the handling of situations where steering is ineffective or even harmful. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
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15 pages, 6865 KiB  
Article
Method for Bottle Opening with a Dual-Arm Robot
by Francisco J. Naranjo-Campos, Juan G. Victores and Carlos Balaguer
Biomimetics 2024, 9(9), 577; https://doi.org/10.3390/biomimetics9090577 - 23 Sep 2024
Viewed by 613
Abstract
This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this [...] Read more.
This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this paper is to propose a method involving vision, manipulation, and learning techniques to effectively address the task of bottle opening. The process begins with the acquisition of bottle and cap positions using an RGB-D camera and computer vision. Subsequently, the robot picks the bottle with one gripper and grips the cap with the other, each by planning safe trajectories. Then, the opening procedure is executed via a position and force control scheme that ensures both grippers follow the unscrewing path defined by the cap thread. Within the control loop, force sensor information is employed to control the vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal angle increments for rotation. The results demonstrate the successful training of the learning agent. The experiments confirm the effectiveness of the proposed method in bottle opening with the TIAGo++ robot, showcasing the practical viability of the approach. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
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34 pages, 9346 KiB  
Article
An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications
by Xiong Wang, Yi Zhang, Changbo Zheng, Shuwan Feng, Hui Yu, Bin Hu and Zihan Xie
Biomimetics 2024, 9(9), 519; https://doi.org/10.3390/biomimetics9090519 - 29 Aug 2024
Viewed by 854
Abstract
The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these [...] Read more.
The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
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14 pages, 5232 KiB  
Article
Computer-Aided Design of 3D-Printed Clay-Based Composite Mortars Reinforced with Bioinspired Lattice Structures
by Nikolaos Kladovasilakis, Sotirios Pemas and Eleftheria Maria Pechlivani
Biomimetics 2024, 9(7), 424; https://doi.org/10.3390/biomimetics9070424 - 11 Jul 2024
Cited by 1 | Viewed by 1190
Abstract
Towards a sustainable future in construction, worldwide efforts aim to reduce cement use as a binder core material in concrete, addressing production costs, environmental concerns, and circular economy criteria. In the last decade, numerous studies have explored cement substitutes (e.g., fly ash, silica [...] Read more.
Towards a sustainable future in construction, worldwide efforts aim to reduce cement use as a binder core material in concrete, addressing production costs, environmental concerns, and circular economy criteria. In the last decade, numerous studies have explored cement substitutes (e.g., fly ash, silica fume, clay-based materials, etc.) and methods to mimic the mechanical performance of cement by integrating polymeric meshes into their matrix. In this study, a systemic approach incorporating computer aid and biomimetics is utilized for the development of 3D-printed clay-based composite mortar reinforced with advanced polymeric bioinspired lattice structures, such as honeycombs and Voronoi patterns. These natural lattices were designed and integrated into the 3D-printed clay-based prisms. Then, these configurations were numerically examined as bioinspired lattice applications under three-point bending and realistic loading conditions, and proper Finite Element Models (FEMs) were developed. The extracted mechanical responses were observed, and a conceptual redesign of the bioinspired lattice structures was conducted to mitigate high-stress concentration regions and optimize the structures’ overall mechanical performance. The optimized bioinspired lattice structures were also examined under the same conditions to verify their mechanical superiority. The results showed that the clay-based prism with honeycomb reinforcement revealed superior mechanical performance compared to the other and is a suitable candidate for further research. The outcomes of this study intend to further research into non-cementitious materials suitable for industrial and civil applications. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
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19 pages, 4600 KiB  
Article
An Enhanced Tree-Seed Algorithm for Function Optimization and Production Optimization
by Qingan Zhou, Rong Dai, Guoxiao Zhou, Shenghui Ma and Shunshe Luo
Biomimetics 2024, 9(6), 334; https://doi.org/10.3390/biomimetics9060334 - 31 May 2024
Viewed by 812
Abstract
As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying [...] Read more.
As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying on gradient data. Among these, the tree-seed algorithm (TSA) distinguishes itself due to its unique mechanism and efficient searching capabilities. However, an imbalance between its exploitation and exploration phases can lead it to be stuck in local optima, impeding the discovery of globally optimal solutions. This study introduces an improved TSA that incorporates water-cycling and quantum rotation-gate mechanisms. These enhancements assist the algorithm in escaping local peaks and achieving a more harmonious balance between its exploitation and exploration phases. Comparative experimental evaluations, using the CEC 2017 benchmarks and a well-known metaheuristic algorithm, demonstrate the upgraded algorithm’s faster convergence rate and enhanced ability to locate global optima. Additionally, its application in optimizing reservoir production models underscores its superior performance compared to competing methods, further validating its real-world optimization capabilities. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
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32 pages, 905 KiB  
Article
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning
by Rodrigo Olivares, Omar Salinas, Camilo Ravelo, Ricardo Soto and Broderick Crawford
Biomimetics 2024, 9(6), 307; https://doi.org/10.3390/biomimetics9060307 - 21 May 2024
Viewed by 1042
Abstract
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, [...] Read more.
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm—with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning’s potential to boost cybersecurity measures in rapidly evolving threat environments. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
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