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Biomimetics
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Optimal Design Approaches of Bioinspired Robots
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Optimal Design Approaches of Bioinspired Robots

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

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 3768

Special Issue Editors

Dr. Guoniu Zhu

E-Mail Website
Guest Editor
Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Interests: task and motion planning; grasping and dexterous manipulation; biologically inspired design
Dr. Yi Fang

E-Mail Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: industrial robots; motion planning and control; multi-objective intelligent optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Yang

E-Mail Website
Guest Editor
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China
Interests: creative soft robot designs; bioinspired soft robots; pneumatic soft robots; magnetic soft robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, entitled "Optimal Design Approaches of Bioinspired Robots," in the journal Biomimetics. Bioinspired robotics has emerged as a fascinating and rapidly growing field that draws inspiration from nature to develop innovative and efficient robotic systems. By studying and mimicking the remarkable abilities of biological systems, researchers aim to create robots with enhanced performance, adaptability, and resilience. This research area holds immense potential for advancing robotic technologies and developing solutions to complex challenges in various domains, such as healthcare, environmental monitoring, search and rescue, and space exploration.

This Special Issue aims to showcase the latest advances and trends in the optimal design approaches for bioinspired robots. We seek to compile a collection of high-quality research articles that explore novel design principles, materials, control systems, and optimization techniques inspired by biological systems. The scope of this Special Issue aligns well with the journal's focus on cutting-edge research in robotics, biomimetics, and intelligent systems. By bringing together a diverse range of contributions from experts in the field, we aim to provide a comprehensive overview of the current state of the art and future directions in bioinspired robot design.

Original research articles and reviews are welcome for this Special Issue. Research areas may include, but are not limited to, the following:

  • Bioinspired design principles and methodologies for robots;
  • Optimal design of bioinspired sensors, actuators, and control systems;
  • Bioinspired materials and structures for soft robotics;
  • Evolutionary algorithms and swarm intelligence for robot optimization;
  • Bioinspired locomotion and manipulation strategies;
  • Biohybrid systems and bioinspired human–robot interaction;
  • Bioinspired energy harvesting and self-healing mechanisms;
  • Applications of bioinspired robots in healthcare, environmental monitoring, and exploration;
  • Bioinspired artificial intelligence and machine learning for robot control and perception;
  • Bioinspired optimization techniques for robot design and performance enhancement.

We look forward to receiving your contributions to this Special Issue and working together to advance the field of bioinspired robotics.

Dr. Guoniu Zhu
Dr. Yi Fang
Dr. Yang Yang
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

  • bioinspired robotics
  • biomimetic design
  • biologically inspired materials
  • bioinspired control systems
  • soft robotics
  • artificial muscles
  • compliant mechanisms
  • swarm intelligence

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Published Papers (4 papers)

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Research

29 pages, 11023 KiB  
Article
Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm
by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng and Yi Fang
Biomimetics 2024, 9(11), 711; https://doi.org/10.3390/biomimetics9110711 - 19 Nov 2024
Viewed by 577
Abstract
Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based [...] Read more.
Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method’s robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference. Full article
(This article belongs to the Special Issue Optimal Design Approaches of Bioinspired Robots)
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34 pages, 11454 KiB  
Article
Compassionate Care with Autonomous AI Humanoid Robots in Future Healthcare Delivery: A Multisensory Simulation of Next-Generation Models
by Joannes Paulus Tolentino Hernandez
Biomimetics 2024, 9(11), 687; https://doi.org/10.3390/biomimetics9110687 - 11 Nov 2024
Viewed by 941
Abstract
The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as “caring”, necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential [...] Read more.
The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as “caring”, necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human–robot interactions (HRIs) surpassing Tetsuya Tanioka’s TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers’ theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of “dynamic communication”, (relational) “entanglement”, and (healthcare system and robot’s function) “superpositioning” in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson’s ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of “caring”. Establishing “pluralistic agreements” through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a “posthumanist” framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by “technological knowing” in Rozzano Locsin’s TCCN theory. Full article
(This article belongs to the Special Issue Optimal Design Approaches of Bioinspired Robots)
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17 pages, 6583 KiB  
Article
A Pneumatic Soft Exoskeleton System Based on Segmented Composite Proprioceptive Bending Actuators for Hand Rehabilitation
by Kai Li, Daohui Zhang, Yaqi Chu, Xingang Zhao, Shuheng Ren and Xudong Hou
Biomimetics 2024, 9(10), 638; https://doi.org/10.3390/biomimetics9100638 - 18 Oct 2024
Viewed by 774
Abstract
Soft pneumatic actuators/robotics have received significant interest in the medical and health fields, due to their intrinsic elasticity and simple control strategies for enabling desired interactions. However, current soft hand pneumatic exoskeletons often exhibit uniform deformation, mismatch the profile of the interacting objects, [...] Read more.
Soft pneumatic actuators/robotics have received significant interest in the medical and health fields, due to their intrinsic elasticity and simple control strategies for enabling desired interactions. However, current soft hand pneumatic exoskeletons often exhibit uniform deformation, mismatch the profile of the interacting objects, and seldom quantify the assistive effects during activities of daily life (ADL), such as extension angle and predicted joint stiffness. The lack of quantification poses challenges to the effective and sustainable advancement of rehabilitation technology. This paper introduces the design, modeling, and testing of pneumatic bioinspired segmented composite proprioceptive bending actuators (SCPBAs) for hand rehabilitation in ADL tasks. Inspired by human finger anatomy, the actuator’s soft-joint–rigid-bone segmented structure provides a superior fit compared to continuous structures in traditional fiber-reinforced actuators (FRAs). A quasi-static model is established to predict the bending angles based on geometric parameters. Quantitative evaluations of predicted joint stiffness and extension angle utilizing proprioceptive bending are performed. Additionally, a soft under-actuated hand exoskeleton equipped with SCPBAs demonstrates their potential in ADL rehabilitation scenarios. Full article
(This article belongs to the Special Issue Optimal Design Approaches of Bioinspired Robots)
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25 pages, 2015 KiB  
Article
Enhancing Target Tracking: A Novel Grid-Based Beetle Antennae Search Algorithm and Confusion-Aware Detection
by Yixuan Lu, Chencong Ma and Dechao Chen
Biomimetics 2024, 9(9), 567; https://doi.org/10.3390/biomimetics9090567 - 19 Sep 2024
Viewed by 645
Abstract
Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a [...] Read more.
Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm’s rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system’s robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model’s high discriminative ability. The results also indicate that the model meets the real-time requirements. Full article
(This article belongs to the Special Issue Optimal Design Approaches of Bioinspired Robots)
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