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Biomimetics
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Biomimetics and Bioinspired Artificial Intelligence Applications
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Biomimetics and Bioinspired Artificial Intelligence Applications

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: closed (30 June 2024) | Viewed by 8884

Special Issue Editors

Prof. Dr. Chaoran Cui

E-Mail Website
Guest Editor
School of Computer Science and Technology, Shandong University of Finance and Economics, No. 7366, East Second Ring Road, Yaojia Sub-District, Jinan 250014, China
Interests: machine learning; data mining; multimedia processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaohui Han

E-Mail Website
Guest Editor
Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Interests: machine learning; data mining; multimedia processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomimetics focuses on living systems and attempts to transfer their properties to engineering applications and has dramatically influenced human civilization. In recent decades, the integration of biomimetics and computing methods has achieved great results in a variety of artificial intelligence applications, including medical diagnosis, robotics, optimization, and pattern recognition. This Special Issue seeks to understand how to design biomimetic machinery and material models that mimic the properties and structures of organisms and report the latest advances in the area of bioinspired algorithms in artificial intelligence. We welcome the manuscripts devoted to the original research, meta-analysis, and review articles related to these directions. Potential topics include, but are not limited to:

  • Biomimetics of materials and structures;
  • Biomimetic design, construction, and devices;
  • Bioinspired robotics and autonomous systems;
  • Applications of bioinspired methods in computer vision and signal processing;
  • Brain-inspired computing methods, e.g., neural networks and deep learning;
  • Swarm intelligence and collective behaviour, e.g., particle swarm optimization and ant colony optimization;
  • Evolutionary algorithms and optimization, e.g., genetic algorithms;
  • Adaptive and self-learning systems.

Prof. Dr. Chaoran Cui
Dr. Xiaohui Han
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

  • biomimetics of materials and structures
  • biomimetic design, construction, and devices
  • bioinspired robotics and autonomous systems
  • applications of bioinspired methods in computer vision and signal processing
  • brain-inspired computing methods, e.g., neural networks and deep learning
  • swarm intelligence and collective behaviour, e.g., particle swarm optimization and ant colony optimization
  • evolutionary algorithms and optimization, e.g., genetic algorithms
  • adaptive and self-learning systems

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Related Special Issue

Published Papers (7 papers)

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Research

20 pages, 97741 KiB  
Article
A Deep Learning Biomimetic Milky Way Compass
by Yiting Tao, Michael Lucas, Asanka Perera, Samuel Teague, Timothy McIntyre, Titilayo Ogunwa, Eric Warrant and Javaan Chahl
Biomimetics 2024, 9(10), 620; https://doi.org/10.3390/biomimetics9100620 - 12 Oct 2024
Viewed by 603
Abstract
Moving in straight lines is a behaviour that enables organisms to search for food, move away from threats, and ultimately seek suitable environments in which to survive and reproduce. This study explores a vision-based technique for detecting a change in heading direction using [...] Read more.
Moving in straight lines is a behaviour that enables organisms to search for food, move away from threats, and ultimately seek suitable environments in which to survive and reproduce. This study explores a vision-based technique for detecting a change in heading direction using the Milky Way (MW), one of the navigational cues that are known to be used by night-active insects. An algorithm is proposed that combines the YOLOv8m-seg model and normalised second central moments to calculate the MW orientation angle. This method addresses many likely scenarios where segmentation of the MW from the background by image thresholding or edge detection is not applicable, such as when the moon is substantial or when anthropogenic light is present. The proposed YOLOv8m-seg model achieves a segment [email protected] of 84.7% on the validation dataset using our own training dataset of MW images. To explore its potential role in autonomous system applications, we compare night sky imagery and GPS heading data from a field trial in rural South Australia. The comparison results show that for short-term navigation, the segmented MW image can be used as a reliable orientation cue. There is a difference of roughly 5–10° between the proposed method and GT as the path involves left or right 90° turns at certain locations. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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39 pages, 3089 KiB  
Article
Two New Bio-Inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours
by Fevzi Tugrul Varna and Phil Husbands
Biomimetics 2024, 9(9), 538; https://doi.org/10.3390/biomimetics9090538 - 5 Sep 2024
Cited by 1 | Viewed by 1037
Abstract
This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The [...] Read more.
This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation. The second algorithm, AHPSO, conceptualises particles in the swarm as energy-driven agents with bio-inspired altruistic behaviour, which allows for the formation of lending–borrowing relationships. The mechanisms underlying these algorithms provide new approaches to maintaining swarm diversity, which contributes to the prevention of premature convergence. The new algorithms were tested on the 30, 50 and 100-dimensional CEC’13, CEC’14 and CEC’17 test suites and various constrained real-world optimisation problems, as well as against 13 well-known PSO variants, the CEC competition winner, differential evolution algorithm L-SHADE and the recent bio-inspired I-CPA metaheuristic. The experimental results show that both the BEPSO and AHPSO algorithms provide very competitive performance on the unconstrained test suites and the constrained real-world problems. On the CEC13 test suite, across all dimensions, both BEPSO and AHPSO performed statistically significantly better than 10 of the 15 comparator algorithms, while none of the remaining 5 algorithms performed significantly better than either BEPSO or AHPSO. On the CEC17 test suite, on the 50D and 100D problems, both BEPSO and AHPSO performed statistically significantly better than 11 of the 15 comparator algorithms, while none of the remaining 4 algorithms performed significantly better than either BEPSO or AHPSO. On the constrained problem set, in terms of mean rank across 30 runs on all problems, BEPSO was first, and AHPSO was third. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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26 pages, 36542 KiB  
Article
Apple-Harvesting Robot Based on the YOLOv5-RACF Model
by Fengwu Zhu, Weijian Zhang, Suyu Wang, Bo Jiang, Xin Feng and Qinglai Zhao
Biomimetics 2024, 9(8), 495; https://doi.org/10.3390/biomimetics9080495 - 14 Aug 2024
Viewed by 883
Abstract
To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot’s locomotion system, Lidar mapping, and navigation, as well as [...] Read more.
To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot’s locomotion system, Lidar mapping, and navigation, as well as the robotic arm’s posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy ([email protected]) of 98.748% and a ([email protected]:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1–3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system’s efficiency and reliability in real agricultural environments. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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22 pages, 12110 KiB  
Article
Learning a Memory-Enhanced Multi-Stage Goal-Driven Network for Egocentric Trajectory Prediction
by Xiuen Wu, Sien Li, Tao Wang, Ge Xu and George Papageorgiou
Biomimetics 2024, 9(8), 462; https://doi.org/10.3390/biomimetics9080462 - 31 Jul 2024
Viewed by 1036
Abstract
We propose a memory-enhanced multi-stage goal-driven network (ME-MGNet) for egocentric trajectory prediction in dynamic scenes. Our key idea is to build a scene layout memory inspired by human perception in order to transfer knowledge from prior experiences to the current scenario in a [...] Read more.
We propose a memory-enhanced multi-stage goal-driven network (ME-MGNet) for egocentric trajectory prediction in dynamic scenes. Our key idea is to build a scene layout memory inspired by human perception in order to transfer knowledge from prior experiences to the current scenario in a top-down manner. Specifically, given a test scene, we first perform scene-level matching based on our scene layout memory to retrieve trajectories from visually similar scenes in the training data. This is followed by trajectory-level matching and memory filtering to obtain a set of goal features. In addition, a multi-stage goal generator takes these goal features and uses a backward decoder to produce several stage goals. Finally, we integrate the above steps into a conditional autoencoder and a forward decoder to produce trajectory prediction results. Experiments on three public datasets, JAAD, PIE, and KITTI, and a new egocentric trajectory prediction dataset, Fuzhou DashCam (FZDC), validate the efficacy of the proposed method. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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20 pages, 3389 KiB  
Article
Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics
by Nicolás J. Garrido, Félix González-Martínez, Susana Losada, Adrián Plaza, Eneida del Olmo and Jorge Mateo
Biomimetics 2024, 9(7), 440; https://doi.org/10.3390/biomimetics9070440 - 18 Jul 2024
Viewed by 1552
Abstract
Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed [...] Read more.
Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission. Materials and Methods: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. Results: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. Conclusions: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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12 pages, 1739 KiB  
Article
Neural Simulation of Actions for Serpentine Robots
by Pietro Morasso
Biomimetics 2024, 9(7), 416; https://doi.org/10.3390/biomimetics9070416 - 7 Jul 2024
Viewed by 1174
Abstract
The neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects of challenging skills. In previous studies, we developed an approach based on the animation of [...] Read more.
The neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects of challenging skills. In previous studies, we developed an approach based on the animation of the redundant human body schema, based on the Passive Motion Paradigm (PMP). In this paper, we show that this approach can be easily extended to hyper-redundant serpentine robots as well as to hybrid configurations where the serpentine robot is functionally integrated with a traditional skeletal infrastructure. A simulation model is analyzed in detail, showing that it incorporates spatio-temporal features discovered in the biomechanical studies of biological hydrostats, such as the elephant trunk or octopus tentacles. It is proposed that such a generative internal model could be the basis for a cognitive architecture appropriate for serpentine robots, independent of the underlying design and control technologies. Although robotic hydrostats have received a lot of attention in recent decades, the great majority of research activities have been focused on the actuation/sensorial/material technologies that can support the design of hyper-redundant soft/serpentine robots, as well as the related control methodologies. The cognitive level of analysis has been limited to motion planning, without addressing synergy formation and mental time travel. This is what this paper is focused on. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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22 pages, 5251 KiB  
Article
Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services
by Gabriel Ioan Arcas, Tudor Cioara and Ionut Anghel
Biomimetics 2024, 9(5), 302; https://doi.org/10.3390/biomimetics9050302 - 18 May 2024
Cited by 1 | Viewed by 1483
Abstract
As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable [...] Read more.
As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services’ computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time. Full article
(This article belongs to the Special Issue Biomimetics and Bioinspired Artificial Intelligence Applications)
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