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Biomimetics
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Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization...
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Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI

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

Deadline for manuscript submissions: 20 January 2025 | Viewed by 7071

Special Issue Editors

Dr. Ameer Tamoor Khan

E-Mail Website
Guest Editor
Department of Plant and Environmental Science, University of Copenhagen, Copenhagen, Denmark
Interests: artificial neural networks; multi-robots; machine learning; dynamic systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuai Li

E-Mail Website
Guest Editor
Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
Interests: robotic manipulation; autonomous manufacturing; multi-robot coordination; intelligent control and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Bolin Liao

E-Mail Website
Guest Editor
College of Computer Science and Engineering, Jishou University, Jishou 416000, China
Interests: controller design; robotics; dynamic systems; control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the fusion of bio-inspired methodologies with data-driven techniques has created a transformative wave that is sweeping across various engineering disciplines. This unique combination has ushered in a new era of innovation, especially in fields such as control systems, optimization techniques, and artificial intelligence (AI) applications. Traditional engineering practices, which once relied heavily on theoretical models and empirical analyses, have now begun to harness the power of data-driven approaches, complemented by insights drawn from biological systems, to enhance their efficiency, accuracy, and adaptability.

This Special Issue aims to examine the forefront of this paradigm shift, focusing on bio-inspired, data-driven methodologies within the realm of engineering. Specifically targeting control, optimization, and AI, this Special Issue seeks to unravel the complex interplay between data-driven techniques and insights from the natural world. The profound impact of integrating bio-inspired concepts with data-driven methodologies in engineering cannot be overstated. Through the lens of control systems, these approaches offer dynamic, adaptive solutions that mimic biological systems, enabling the precise and responsive management of complex systems. Optimization techniques, bolstered by data-driven insights and bio-inspired algorithms, push the boundaries of traditional methods, unlocking new avenues for efficiency and performance enhancement across various engineering fields. Meanwhile, AI applications, inspired by the cognitive and adaptive capabilities of biological organisms, are set to revolutionize engineering practices with their intelligent, self-optimizing solutions.

However, this promising convergence of bio-inspired and data-driven methodologies with engineering practices presents significant challenges. Achieving harmonious integration requires a deep understanding of biological principles, data science, and engineering disciplines, as well as the ability to contend with issues of interpretability, reliability, and scalability. Moreover, this fusion requires robust frameworks for data collection, preprocessing, and analysis, ensuring that insights drawn from vast datasets are both relevant and aligned with bio-inspired principles.

This Special Issue invites contributions that explore the cutting-edge bio-inspired, data-driven methodologies emerging in engineering. Through collaborative research, practical applications, and theoretical discussions, we aim to highlight the transformative potential of these methodologies. By fostering dialogue, sharing insights, and promoting further advancements, this Special Issue seeks to chart a course towards a future where engineering practices are empowered by the synergy between bio-inspired insights and data-driven approaches, achieving unprecedented levels of efficacy, adaptability, and innovation.

We welcome original research contributions addressing, but not limited to, the following topics:

  1. The design and implementation of bio-inspired, data-driven control systems;
  2. Machine learning and deep learning techniques for the optimization of engineering, inspired by natural processes;
  3. Reinforcement learning approaches to control and optimization problems, with insights drawn from biological systems;
  4. The use of bio-inspired methodologies for data-driven modelling and predictive maintenance in engineering systems;
  5. The integration of bio-inspired methods with traditional control and optimization techniques;
  6. Applications of bio-inspired, data-driven approaches in robotics, automation, and autonomous systems;
  7. Case studies and real-world applications showcasing the effectiveness of bio-inspired, data-driven methods in different engineering domains;
  8. The interpretability, robustness, and reliability of bio-inspired, data-driven control, optimization, and AI techniques.

Dr. Ameer Tamoor Khan
Prof. Dr. Shuai Li
Dr. Bolin Liao
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

  • bio-inspired
  • biomimetics
  • optimization
  • data-driven
  • machine learning
  • LLM

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Published Papers (7 papers)

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Research

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22 pages, 4829 KiB  
Article
Robot Task-Constrained Optimization and Adaptation with Probabilistic Movement Primitives
by Guanwen Ding, Xizhe Zang, Xuehe Zhang, Changle Li, Yanhe Zhu and Jie Zhao
Biomimetics 2024, 9(12), 738; https://doi.org/10.3390/biomimetics9120738 - 3 Dec 2024
Viewed by 586
Abstract
Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from [...] Read more.
Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from a small number of human demonstrations and adapt to different task constraints, including waypoints, joint limits, virtual walls, and obstacles. Probabilistic Movement Primitives (ProMPs) model movements with distributions, thus providing the robot with additional freedom for task execution. We provide the robot with three modes to move, with only one human demonstration required for each mode. We propose an improved via-point generalization method to generalize smooth trajectories with encoded ProMPs. In addition, we present an effective task-constrained optimization method that incorporates all task constraints analytically into a probabilistic framework. We separate ProMPs as Gaussians at each timestep and minimize Kullback–Leibler (KL) divergence, with a gradient ascent–descent algorithm performed to obtain optimized ProMPs. Given optimized ProMPs, we outline a unified robot movement adaptation method for extending from a single obstacle to multiple obstacles. We validated our approach with a 7-DOF Xarm robot using a series of movement adaptation experiments. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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20 pages, 3256 KiB  
Article
Application of Real-Time Palm Imaging with Nelder–Mead Particle Swarm Optimization/Regression Algorithms for Non-Contact Blood Pressure Detection
by Te-Jen Su, Ya-Chung Hung, Wei-Hong Lin, Wen-Rong Yang, Qian-Yi Zhuang, Yan-Xiang Fei and Shih-Ming Wang
Biomimetics 2024, 9(11), 713; https://doi.org/10.3390/biomimetics9110713 - 20 Nov 2024
Viewed by 671
Abstract
In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the “silent killer” nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study [...] Read more.
In response to the rising prevalence of hypertension due to lifestyle changes, this study introduces a novel approach for non-contact blood pressure (BP) monitoring. Recognizing the “silent killer” nature of hypertension, this research focuses on developing accessible, non-invasive BP measurement methods. This study compares two distinct non-contact BP measurement approaches: one combining the Nelder–Mead simplex method with particle swarm optimization (NM-PSO) and the other using machine learning regression analysis. In the NM-PSO method, a standard webcam captures continuous images of the palm, extracting physiological data through light wave reflection and employing independent component analysis (ICA) to remove noise artifacts. The NM-PSO achieves a verified root mean square error (RMSE) of 2.71 mmHg for systolic blood pressure (SBP) and 3.42 mmHg for diastolic blood pressure (DBP). Alternatively, the regression method derives BP values through machine learning-based regression formulas, resulting in an RMSE of 2.88 mmHg for SBP and 2.60 mmHg for DBP. Both methods enable fast, accurate, and convenient BP measurement within 10 s, suitable for home use. This study demonstrates a cost-effective solution for non-contact BP monitoring and highlights each method’s advantages. The NM-PSO approach emphasizes optimization in noise handling, while the regression method leverages formulaic efficiency in BP estimation. These results offer a biomimetic approach that could replace traditional contact-based BP measurement devices, contributing to enhanced accessibility in hypertension management. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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19 pages, 9978 KiB  
Article
Analysis of Cushioned Landing Strategies of Cats Based on Posture Estimation
by Li Zhang, Liangliang Han, Haohang Liu, Rui Shi, Meiyang Zhang, Weijun Wang and Xuyan Hou
Biomimetics 2024, 9(11), 691; https://doi.org/10.3390/biomimetics9110691 - 13 Nov 2024
Viewed by 663
Abstract
This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking [...] Read more.
This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking it using animal posture estimation, we derive the cushioning strategy that cats use to handle landing impacts. The results indicate that the strategy effectively transforms high-intensity impacts into prolonged low-intensity impacts, thereby safeguarding the brain and internal organs. We adapt this cushioning strategy for robotic platforms through reasonable assumptions and simplifications. Simulations are conducted in both gravitational and zero gravity environments, demonstrating that the optimized strategy not only reduces ground impact and prolongs the cushioning duration but also effectively suppresses the robot’s rebound. In zero gravity, the strategy enhances stable attachment to target surfaces. This research introduces a novel biomimetic control strategy for landing control in the on-orbit operations of space robots. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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16 pages, 3840 KiB  
Article
Oxygen-Plasma-Treated Al/TaOX/Al Resistive Memory for Enhanced Synaptic Characteristics
by Gyeongpyo Kim, Seoyoung Park, Minsuk Koo and Sungjun Kim
Biomimetics 2024, 9(9), 578; https://doi.org/10.3390/biomimetics9090578 - 23 Sep 2024
Viewed by 822
Abstract
In this study, we investigate the impact of O2 plasma treatment on the performance of Al/TaOX/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences [...] Read more.
In this study, we investigate the impact of O2 plasma treatment on the performance of Al/TaOX/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences in chemical composition between O2-plasma-treated and untreated RRAM cells. Direct-current measurements showed that O2-plasma-treated RRAM cells exhibited significant improvements over untreated RRAM cells, including higher on/off ratios, improved uniformity and distribution, longer retention times, and enhanced durability. The conduction mechanism is investigated by current–voltage (I–V) curve fitting. In addition, paired-pulse facilitation (PPF) is observed using partial short-term memory. Furthermore, 3- and 4-bit weight tuning with auto-pulse-tuning algorithms was achieved to improve the controllability of the synapse weight for the neuromorphic system, maintaining retention times exceeding 103 s in the multiple states. Neuromorphic simulation with an MNIST dataset is conducted to evaluate the synaptic device. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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22 pages, 1097 KiB  
Article
Virtual Simulation-Based Optimization for Assembly Flow Shop Scheduling Using Migratory Bird Algorithm
by Wen-Bin Zhao, Jun-Han Hu and Zi-Qiao Tang
Biomimetics 2024, 9(9), 571; https://doi.org/10.3390/biomimetics9090571 - 21 Sep 2024
Viewed by 767
Abstract
As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks [...] Read more.
As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm’s optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5–6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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21 pages, 1175 KiB  
Article
Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency
by Zanyu Tang, Mingzhi Mao, Yunong Zhang and Ning Tan
Biomimetics 2024, 9(7), 435; https://doi.org/10.3390/biomimetics9070435 - 17 Jul 2024
Viewed by 795
Abstract
Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming [...] Read more.
Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming from angle-layer and velocity-layer constraints to acceleration-layer constraints, five theorems and three corollaries are theoretically derived and rigorously proved. Then, together with the unified acceleration-layer bound constraint, an enhanced acceleration-layer CA scheme specially considering three-layer time-variant physical limits is proposed, and a simplified acceleration-layer CA scheme considering three-layer time-invariant physical limits is also proposed. The proposed CA schemes are finally formulated in the form of standard quadratic programming and are solved by a projection neurodynamics solver. Moreover, comparative simulative experiments based on a four-link planar arm and a UR3 spatial arm are performed to verify the efficacy and superiority of the proposed CA schemes. At last, physical experiments are conducted on a real Kinova Jaco2 arm to substantiate the practicability of the proposed CA schemes. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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Review

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22 pages, 1932 KiB  
Review
Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration
by Zhewen Zhang, Peng Xu, Chengjia Wu and Hongliu Yu
Biomimetics 2024, 9(8), 492; https://doi.org/10.3390/biomimetics9080492 - 14 Aug 2024
Cited by 1 | Viewed by 1561
Abstract
As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications [...] Read more.
As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications and prospects of smart nursing wheelchairs, with particular emphasis on their assistive nursing functions, multiple-sensor fusion technology, and human–machine interaction interfaces. First, we describe the assistive functions of nursing wheelchairs, including position changing, transferring, bathing, and toileting, which significantly reduce the workload of nursing staff and improve the quality of care. Second, we summarized the existing multiple-sensor fusion technology for smart nursing wheelchairs, including LiDAR, RGB-D, ultrasonic sensors, etc. These technologies give wheelchairs autonomy and safety, better meeting patients’ needs. We also discussed the human–machine interaction interfaces of intelligent care wheelchairs, such as voice recognition, touch screens, and remote controls. These interfaces allow users to operate and control the wheelchair more easily, improving usability and maneuverability. Finally, we emphasized the importance of multifunctional-integrated care wheelchairs that integrate assistive care, navigation, and human–machine interaction functions into a comprehensive care solution for users. We are looking forward to the future and assume that smart nursing wheelchairs will play an increasingly important role in medicine and geriatric care. By integrating advanced technologies such as enhanced artificial intelligence, intelligent sensors, and remote monitoring, we expect to further improve patients’ quality of care and quality of life. Full article
(This article belongs to the Special Issue Bio-Inspired Data-Driven Methods and Their Applications in Engineering Control, Optimization and AI)
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