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Biomimetics
Special Issues
Data-Centric Engineering for Sustainable Future with AI and Human-in-the-Loop
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Data-Centric Engineering for Sustainable Future with AI and Human-in-the-Loop

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 1647

Special Issue Editors

Dr. Yi Chen

E-Mail Website
Guest Editor
School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, Suzhou 15400, China
Interests: AI; digital manufacturing; robotics; autonomous system; high-performance computing
Special Issues, Collections and Topics in MDPI journals
Dr. Dehong Huo

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: digital manufacturing; precision machining; manufacturing instrumentation; machining process simulation
Dr. Xiangrong Su

E-Mail Website
Guest Editor
Cummins Generator Technology, Peterborough PE2 6FZ, UK
Interests: Al; digital manufacturing; turbo machinery; noise and vibration

Special Issue Information

Dear Colleagues,

The 2025 IEEE Congress on Evolutionary Computation (CEC2025 https://www.cec2025.org) is a prestigious international event in the domain of Evolutionary Computation. It offers a platform for researchers and practitioners to come together and share their latest findings on Evolutionary Computation, covering areas such as algorithms, machine learning for optimisation and evolutionary learning, optimisation, and theoretical aspects.

The CEC2025 is excited to announce a Special Issue dedicated to the innovative applications of evolutionary computation across various disciplines, with a particular focus on sustainable futures. The session, "Evolutionary Computation in Multidisciplinary Applications for Sustainable Future (ECMASF, https://github.com/CIAD-LAB/IEEECEC2025-ECMASF)", aims to connect evolutionary computation with sustainability, encouraging innovative solutions to tackle global challenges.

Under the same editorial team and with the same scope, we extend a selective invitation to authors who have presented their research at the conference to submit enhanced versions of their work for consideration in prestigious peer-reviewed journals. These manuscripts must demonstrate significant advancement, with a minimum of 50% of original content beyond the conference paper.

1. Introduction and Scope:

This Special Issue explores the convergence of artificial intelligence (AI), robotics, and human-in-the-loop methodologies in developing sustainable engineering solutions. It focuses on integrating biomimetic principles with AI and human-centred design to address complex sustainability challenges in manufacturing, energy systems, and environmental management.

2. Topics of Interest:

We invite original research, reviews, and case studies on, but not limited to, the following:

  • AI-driven sustainable engineering design;
  • High-precision intelligent manufacturing with AI;
  • Human–AI collaboration in eco-friendly lean manufacturing;
  • Robotics for environmental monitoring and conservation;
  • Digital twins and cyber–physical systems;
  • High-performance computing in sustainable engineering with AI-in-the-loop;
  • Industry 4.0/5.0 approaches to the circular economy;
  • AI for energy-efficient systems;
  • Human-in-the-loop machine learning for adaptive engineering;
  • AI-enhanced life cycle assessment and product design;
  • AI for multidisciplinary and social science applications;
  • AI-driven energy systems for enhanced efficiency;
  • Lifecycle assessment and design via AI;
  • Bioinspired robotics in ecological restoration;
  • Cross-disciplinary AI applications in social and environmental sciences;
  • Autonomous systems including unmanned aerial vehicles (UAVs), unmanned surface vessels (USVs), autonomous underwater vehicles (AUVs), autonomous driving systems (ADSs), etc.

Importance and Timeliness:

As we transition to Industry 4.0/5.0, this Special Issue provides a platform for sharing cutting-edge research on integrating AI, robotics, and human expertise with biomimetic principles in sustainable engineering.

Dr. Yi Chen
Dr. Dehong Huo
Dr. Xiangrong Su
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

  • artificial intelligence
  • multidisciplinary engineering
  • human-in-the-loop
  • sustainable manufacturing
  • Industry 4.0/5.0
  • digital twins
  • robotics
  • autonomous systems
  • cyber-physical systems
  • nature-inspired algorithms
  • eco-innovation
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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17 pages, 1929 KiB  
Article
Bio-Signal-Guided Robot Adaptive Stiffness Learning via Human-Teleoperated Demonstrations
by Wei Xia, Zhiwei Liao, Zongxin Lu and Ligang Yao
Biomimetics 2025, 10(6), 399; https://doi.org/10.3390/biomimetics10060399 - 13 Jun 2025
Viewed by 460
Abstract
Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the [...] Read more.
Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal–motion correlation and the motion–sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human–robot operational effects after human demonstration. Full article
(This article belongs to the Special Issue Data-Centric Engineering for Sustainable Future with AI and Human-in-the-Loop)
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18 pages, 2770 KiB  
Article
Decentralized Multi-Robot Navigation Based on Deep Reinforcement Learning and Trajectory Optimization
by Yifei Bi, Jianing Luo, Jiwei Zhu, Junxiu Liu and Wei Li
Biomimetics 2025, 10(6), 366; https://doi.org/10.3390/biomimetics10060366 - 4 Jun 2025
Viewed by 593
Abstract
Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this issue, this paper leverages graph neural [...] Read more.
Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this issue, this paper leverages graph neural networks (GNNs) and deep reinforcement learning (DRL) to aggregate high-dimensional features as inputs for reinforcement learning (RL) to generate paths. Additionally, it introduces safety constraints through an artificial potential field (APF) to optimize these trajectories. Additionally, a constrained nonlinear optimization method further refines the APF-adjusted paths, resulting in the development of the GNN-RL-APF-Lagrangian algorithm. By combining APF and nonlinear optimization techniques, experimental results demonstrate that this method significantly enhances the safety and obstacle avoidance capabilities of multi-robot systems in complex environments. The proposed GNN-RL-APF-Lagrangian algorithm achieves a 96.43% success rate in sparse obstacle environments and 89.77% in dense obstacle scenarios, representing improvements of 59% and 60%, respectively, over baseline GNN-RL approaches. The method maintains scalability up to 30 robots while preserving distributed execution properties. Full article
(This article belongs to the Special Issue Data-Centric Engineering for Sustainable Future with AI and Human-in-the-Loop)
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