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Biomimetics
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Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition
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Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3327

Editors

Dr. Pengcheng Liu

E-Mail Website
Guest Editor
Department of Computer Science, University of York, Heslington YO10 5GH, UK
Interests: robot learning; applied control; bioinspiration and biomimetics
Special Issues, Collections and Topics in MDPI journals
Dr. Qinbing Fu

E-Mail Website
Guest Editor
Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, China
Interests: brain-inspired intelligence; motion perception; machine vision; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Tiong Hoo Lim

E-Mail Website
Guest Editor
Faculty of Engineering, Universiti Teknologi Brunei, Mukim Gadong A, Bandar Seri Begawan BE1410, Brunei
Interests: artificial intelligence; autonomous systems; and embedded technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotics is a multidisciplinary research field that demonstrates enormous potential. It concerns developing intelligent robotic systems that are capable of making decisions and acting autonomously in real and dynamic environments to accomplish tasks and assist humans in relevant application domains within our society. Recently, advances in the computational study of intelligent behaviors such as learning and adaptation have led to powerful insights about the nature of learning in both humans, animals, materials, and machines. However, new and challenging theoretical and technological problems are being posed. One can apply the computational metaphor in different ways, and computational learning has become an important topic within many paradigms, including artificial intelligence, pattern recognition, control theory, cognitive intelligence, behavioral intelligence, and statistics. Such a convergence of interests is encouraging, but few researchers in this active area communicate across disciplinary boundaries, and even fewer are skilled in the ‘language’ and techniques of more than one approach. With this new era of computational learning for robotics, substantial continued research is essential in order to advance the field and to evaluate the multidisciplinary concerns posed by learning and adaptation techniques.

The aim of this Special Issue is to highlight the roles of advanced bio-inspired and biomimetic intelligence for robotics applications, with a focus on how prior knowledge contributes to successful outcomes and, in particular, how they contribute to the taming of the complexity of the linked domains. It includes, but is not limited to, the following topics:

  • Behavioral and biological learning and control;
  • Computational neuroscience;
  • Cognitive robotics and computation;
  • Evolutionary robotics, multi-robot systems, and swarm intelligence;
  • Computational modeling of biological systems;
  • Biomechanics, biomechatronics, and bioengineering;
  • Smart materials;
  • Soft robotics and sensing;
  • Human‒robot interaction and collaboration;
  • Bio-inspired approaches for robot design, control, and optimization;
  • Morphological computation and embodied intelligence;
  • Bio-inspired spiking neural networks;
  • Bio-inspired vision systems;
  • Imitation learning and Bayesian/probabilistic learning;
  • Bio-inspired legged robotics;
  • Bio-inspired/biomimetic underwater robotics;
  • Micro- and nano-robotics;
  • Healthcare and rehabilitation;
  • Flexible electronics and piezoelectric actuators.

Dr. Pengcheng Liu
Dr. Qinbing Fu
Dr. Tiong Hoo Lim
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • biological inspiration
  • biomimetics
  • computational learning
  • robotics

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Related Special Issue

Published Papers (5 papers)

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Research

17 pages, 8846 KB  
Article
Fly-by-Feel: Advancements and Applications of Bio-Inspired Wind-Hair Sensors on Fixed-Wing UAVs
by Omar Selim, Alecsandra Court and Christoph Brücker
Biomimetics 2026, 11(7), 500; https://doi.org/10.3390/biomimetics11070500 - 16 Jul 2026
Viewed by 514
Abstract
Distributed aerodynamic sensing is a key requirement for future fly-by-feel UAV systems. Inspired by mechanosensory systems found in flying animals, this paper investigates the use of a bio-inspired optically tracked flexible pillar sensor array for aerodynamic sensing and stall detection on a washed-out [...] Read more.
Distributed aerodynamic sensing is a key requirement for future fly-by-feel UAV systems. Inspired by mechanosensory systems found in flying animals, this paper investigates the use of a bio-inspired optically tracked flexible pillar sensor array for aerodynamic sensing and stall detection on a washed-out NACA0012 aerofoil. Experiments were conducted in a low-speed water tunnel, with flow at chord-based Reynolds Re=70×103 and the pillar sensors set to measure local flow conditions. Sensor calibration and dynamic characterisation were performed prior to testing. Time-resolved flow visualisation measurements were used to validate sensor response and investigate local flow phenomena. The results demonstrated that flexible pillar sensors can capture early indications of stall through monitoring of spanwise mean deflection, flow reversal events associated with incipient and fully separated flow, and characteristic low-frequency oscillations. The findings demonstrate the potential of distributed bio-inspired sensor arrays to enhance stall detection and enable real-time aerodynamic monitoring in future fly-by-feel UAV systems. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition)
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30 pages, 30615 KB  
Article
Enhanced LGMD Model with Adaptive Probabilistic Regulation for Compound Interference
by Hao Luan, Changmiao Nie, Weikun Chen, Bin Yang, Hongwei Li and Jintao Zhao
Biomimetics 2026, 11(7), 488; https://doi.org/10.3390/biomimetics11070488 - 11 Jul 2026
Viewed by 310
Abstract
When subjected to compound interference, such as spatial noise and high-frequency jitter, current LGMD-inspired collision detection models for micro-robots are prone to false alarms and perceptual degradation. To address this challenge, this paper proposes an enhanced visual perception model that incorporates adaptive Gaussian [...] Read more.
When subjected to compound interference, such as spatial noise and high-frequency jitter, current LGMD-inspired collision detection models for micro-robots are prone to false alarms and perceptual degradation. To address this challenge, this paper proposes an enhanced visual perception model that incorporates adaptive Gaussian random variables and spatial residual feedback (SRF). These random variables filter out discrete spatial noise, while the SRF suppresses global image shifts induced by jitter. Evaluations on synthetic and real-world video sequences validate the proposed mechanisms. Comparative results demonstrate that the model effectively reduces false responses under compound interference, thereby maintaining robust success rate (SR), discrimination ratio (DR), and membrane potential stability index (MPSI) metrics. To explain this robustness, ablation analyses further verify the synergistic benefits of the SRF and the Gaussian random variables. Furthermore, statistical results on the random variables indicate that, under compound interference, the adaptive probabilistic model outperforms fixed probabilistic configurations. By ensuring robust collision perception against such interference, this work enhances the practical viability of LGMD-inspired visual systems. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition)
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24 pages, 4952 KB  
Article
Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis
by Jens Grotrian
Biomimetics 2026, 11(6), 437; https://doi.org/10.3390/biomimetics11060437 - 18 Jun 2026
Viewed by 657
Abstract
Empirical logic (EL) is a bio-inspired soft computing approach to rule-based decision-making that emphasizes intuitive, experience-based reasoning. While its theoretical foundations have been established in previous work, its practical applicability and accessibility have so far received less attention. This paper addresses this gap [...] Read more.
Empirical logic (EL) is a bio-inspired soft computing approach to rule-based decision-making that emphasizes intuitive, experience-based reasoning. While its theoretical foundations have been established in previous work, its practical applicability and accessibility have so far received less attention. This paper addresses this gap by providing two representative application examples from distinct domains: control engineering and cluster analysis. The first example demonstrates the use of EL for the speed control of a DC drive, highlighting its ability to achieve competitive dynamic performance with a small number of intuitive rules. The second example introduces a novel approach to cluster analysis, where cluster structures emerge from the collective interaction of EL rules rather than from the optimization of a predefined objective function. In addition, the paper emphasizes the availability of publicly accessible software realizations of EL, including a Maple-based prototype and a Python framework, which enable direct experimentation and practical use. By combining illustrative applications with executable tools, the paper aims to facilitate the transition from conceptual understanding to practical deployment and to support further exploration of EL in applied soft computing contexts. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition)
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24 pages, 1306 KB  
Article
Systematic Evaluation of Biologically Inspired Motion Detection Models: From LGMD and EMD to Hybrid Spiking Neural Networks
by Vanessa Ndiangang and Pengcheng Liu
Biomimetics 2026, 11(6), 374; https://doi.org/10.3390/biomimetics11060374 - 28 May 2026
Viewed by 493
Abstract
Collision detection in dynamic environments demands perception systems that are both computationally efficient and robust to diverse motion patterns. Biological vision systems, particularly those of insects, offer efficient neural architectures capable of rapid motion interpretation under strict resource constraints. This work presents a [...] Read more.
Collision detection in dynamic environments demands perception systems that are both computationally efficient and robust to diverse motion patterns. Biological vision systems, particularly those of insects, offer efficient neural architectures capable of rapid motion interpretation under strict resource constraints. This work presents a systematic comparative evaluation of three biologically inspired models: the Lobula Giant Movement Detector (LGMD), the Elementary Motion Detector (EMD), and a hybrid Spiking Neural Network (SNN) incorporating LGMD and EMD-derived motion processing pathways, evaluated on programmatically generated synthetic stimuli with frame-level ground truth. The hybrid SNN achieved an accuracy of 73–87% across stimulus types, consistently exceeding the 75.0% held-out test set baseline, with a precision of 1.0 throughout and a substantially lower runtime than the LGMD implementation. LGMD demonstrated rate-based sensitivity consistent with biological spike-frequency adaptation, while the EMD correctly produced near-zero responses to looming stimuli, confirming its role as a directional rather than collision detector. These results demonstrate that hybridising biologically inspired motion detectors within a trainable spiking framework produces a promising and reproducible approach to collision prediction, while identifying the sim-to-real generalisation gap as a key challenge for future deployment. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition)
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24 pages, 3942 KB  
Article
Robust Looming Spatial Localization in Dim Light via Daubechies Wavelet-Fused ON/OFF Pathways
by Zefang Chang, Guangrong Wu, Hao Chen, He Zhang, Hao Luan and Zhijian Yang
Biomimetics 2026, 11(4), 244; https://doi.org/10.3390/biomimetics11040244 - 3 Apr 2026
Cited by 1 | Viewed by 524
Abstract
Computational models of the MLG1 neurons in crab Neohelice granulata have been developed to detect and spatially localize looming stimuli. However, existing models suffer from significant performance degradation in dim scenarios, primarily due to visual signal corruption from stochastic noise such as photon [...] Read more.
Computational models of the MLG1 neurons in crab Neohelice granulata have been developed to detect and spatially localize looming stimuli. However, existing models suffer from significant performance degradation in dim scenarios, primarily due to visual signal corruption from stochastic noise such as photon shot noise. To address this challenge, we propose a computational framework that embeds Daubechies wavelet directly into ON/OFF visual pathways. The ON/OFF mechanism separates the input signals in parallel based on luminance changes to capture dynamic differences between target and background. Embedding Daubechies wavelet enables multi-scale frequency decomposition, allowing the model to suppress high-frequency noise while enhancing low-frequency looming trends. This process extracts low-frequency components and high-frequency details, providing the MLG1 neuron with more discriminative feature inputs. Experimental results demonstrate that the model achieves reliable looming spatial localization under extremely low contrast conditions, offering a robust methodology for bionic vision in extreme dim light environments. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 3rd Edition)
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