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Biomimetics
Special Issues
Bio-Inspired Robotics and Applications 2026
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Bio-Inspired Robotics and Applications 2026

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 603

Special Issue Editors

Prof. Dr. Simon X. Yang

E-Mail Website
Guest Editor
Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: intelligent systems; robotics; control systems; sensors and multi-sensor fusion; wireless sensor networks; intelligent communications; intelligent transportation; machine learning; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Junfei Li

E-Mail Website
Guest Editor
Department of Mechanical and Mechatronics Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: robotic collectives; swarm robots; bio-inspired algorithms; human–robot collaborations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biological inspiration provides the basis for many aspects of robotics. The resourceful methodologies of biological organisms have been incorporated into the development of many new methodologies, strategies, and algorithms for robotic systems. The novelty and significance of this new research have provided new knowledge to the respective research communities, which could potentially have many civilian and military applications.

The main goal of this Special Issue is to investigate the fundamental theories of bio-inspired robotics methodologies and to report their novel applications in the field of robotics, such as real-time sensing and multi-sensor fusion, real-time intelligent navigation, the cooperation of multiple robotic systems, simultaneous localization and mapping (SLAM), real-time collision-free path planning, and the tracking and control of a robot.

This Special Issue invites original research and review articles that contribute new knowledge to their respective fields of study. It also aims to provide insights into biologically inspired methodologies that can be applied across various research areas and applications.

Prof. Dr. Simon X. Yang
Dr. Junfei Li
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-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

  • autonomous robotic systems
  • intelligent systems
  • bio-inspired intelligence
  • intelligent control systems
  • intelligent multi-sensor fusion
  • intelligent path planning and tracking
  • intelligent real-time navigation
  • intelligent coordination and cooperation
  • intelligent robot teleoperation

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Published Papers (1 paper)

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Research

30 pages, 4268 KB  
Article
A Bumblebee-Inspired Spatial Memory Navigation Framework for Robotic Odor Source Localization
by Tianyi Xu, Yizhu Guo, Zhigang Wu and Jianing Wu
Biomimetics 2026, 11(5), 350; https://doi.org/10.3390/biomimetics11050350 - 18 May 2026
Viewed by 288
Abstract
Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into [...] Read more.
Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into robotic decision-making. First, classical conditioning and olfactorily triggered spatial memory experiments demonstrated that bumblebees could form robust odor memories and that training frequency is positively correlated with both proboscis extension response retention and spatial directional preference. Based on these biological findings, a bio-inspired navigation framework, termed Bio-Nav, is constructed by integrating a Partially Observable Markov Decision Process, a Hidden Markov Model, short-term memory, long-term directional reference memory, fuzzy inference, and value iteration. High-fidelity two-dimensional turbulent simulations show that the proposed algorithm substantially outperforms moth-inspired search, Infotaxis, and standard POMDP-based navigation. In 100 Monte Carlo trials, Bio-Nav achieved a success rate of 96.0%, an average of 20.3 search steps, an average path length of 155.1 cm, and a path-to-straight-line distance ratio of 1.6. Even under strong turbulence, the success rate remained above 91%. These results indicate that memory–perception coupling, inspired by bumblebee navigation, provides an effective and robust strategy for odor source localization in complex turbulent environments, offering a generalizable principle for bio-inspired robotic search under uncertainty. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2026)
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