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Biomimetics
Special Issues
Bio-Inspired Robotics and Applications 2025
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Bio-Inspired Robotics and Applications 2025

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 4857

Special Issue Editors

Prof. Dr. Simon X. Yang

E-Mail Website
Guest Editor
Advanced Robotics & Intelligent Systems (ARIS) Laboratory, 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
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, 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 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

  • 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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Related Special Issue

Published Papers (6 papers)

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Research

57 pages, 5274 KB  
Article
Aerospace Bionic Robotics: BEAM-D Technical Standard of Biomimetic Engineering Design Methodology Applied to Mechatronics Systems
by Jose Cornejo, Alfredo Weitzenfeld, José Baca and Cecilia E. García Cena
Biomimetics 2025, 10(10), 668; https://doi.org/10.3390/biomimetics10100668 - 5 Oct 2025
Viewed by 655
Abstract
The origin of life initiated an evolutionary continuum yielding biologically optimized systems capable of operating under extreme environmental constraints. Biomimetics, defined as the systematic abstraction and transfer of biological principles into engineering domains, has become a strategic design paradigm for addressing the multifactorial [...] Read more.
The origin of life initiated an evolutionary continuum yielding biologically optimized systems capable of operating under extreme environmental constraints. Biomimetics, defined as the systematic abstraction and transfer of biological principles into engineering domains, has become a strategic design paradigm for addressing the multifactorial challenges of space systems. This study introduces two core contributions to formally establish the discipline of Aerospace Bionic Robotics (ABR): First, it elucidates the relevance of biologically derived functionalities such as autonomy, adaptability, and multifunctionality to enhance the efficiency of space robotic platforms operating in microgravity environments. Second, it proposed the BEAM-D (Biomimetic Engineering and Aerospace Mechatronics Design), a standard for the development of Aerospace Bionic Robotics. By integrating biological abstraction levels (morphological, functional, and behavioral) with engineering protocols including ISO, VDI, and NASA’s TRL, BEAM-D enables a structured design pathway encompassing subsystem specification, cyber–physical integration, in situ testing, and full-scale mission deployment. It is implemented through a modular BEAM-DX framework and reinforced by iterative BIOX design steps. This study thus establishes formalized bio-inspired design tools for advanced orbital and planetary robotic systems capable of sustained autonomous operations in deep space exploration scenarios. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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21 pages, 2281 KB  
Article
Path Optimization for Cluster Order Picking in Warehouse Robotics Using Hybrid Symbolic Control and Bio-Inspired Metaheuristic Approaches
by Mete Özbaltan, Serkan Çaşka, Merve Yıldırım, Cihat Şeker, Faruk Emre Aysal, Hazal Su Bıçakcı Yeşilkaya, Murat Demir and Emrah Kuzu
Biomimetics 2025, 10(10), 657; https://doi.org/10.3390/biomimetics10100657 - 1 Oct 2025
Viewed by 350
Abstract
In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization [...] Read more.
In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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26 pages, 1127 KB  
Article
LSTM-Enhanced TD3 and Behavior Cloning for UAV Trajectory Tracking Control
by Yuanhang Qi, Jintao Hu, Fujie Wang and Gewen Huang
Biomimetics 2025, 10(9), 591; https://doi.org/10.3390/biomimetics10090591 - 4 Sep 2025
Viewed by 745
Abstract
Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning [...] Read more.
Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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20 pages, 14992 KB  
Article
A Lightweight Bioinspired SMA-Based Grasping Mechanism for Flapping Wing MAVs
by Ahmad Hammad, Mehmet Süer and Sophie F. Armanini
Biomimetics 2025, 10(6), 364; https://doi.org/10.3390/biomimetics10060364 - 4 Jun 2025
Cited by 1 | Viewed by 1040
Abstract
This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) [...] Read more.
This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) that mimic muscle movement. These SMA springs act as compact, lightweight substitutes for traditional actuators like motors or solenoids. The mechanism operates via short electrical impulses that trigger both opening and closing motions. A detailed design process was undertaken to optimize phalange lengths for cylindrical grasping and to select appropriate SMAs for reliable performance. Weighing only 50 g, the gripper leverages the high power-to-weight ratio and flexibility of SMAs, with the springs directly embedded within the phalanges to reduce size and mass while preserving high-force output. Experimental results demonstrate fast actuation and a grasping force of approximately 16 N, enabling the gripper to hold objects of varying shapes and sizes and perform perching, grasping, and carrying tasks. Compared to existing solutions, this mechanism offers a simpler, highly integrated structure with enhanced miniaturization and adaptability, making it especially suitable for low-payload MAV platforms like FWMAVs. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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19 pages, 7467 KB  
Article
A Bionic Goal-Oriented Path Planning Method Based on an Experience Map
by Qiang Zou and Yiwei Chen
Biomimetics 2025, 10(5), 305; https://doi.org/10.3390/biomimetics10050305 - 11 May 2025
Viewed by 601
Abstract
Brain-inspired bionic navigation is a groundbreaking technological approach that emulates the biological navigation systems found in mammalian brains. This innovative method leverages experiences within cognitive space to plan global paths to targets, showcasing remarkable autonomy and adaptability to various environments. This work introduces [...] Read more.
Brain-inspired bionic navigation is a groundbreaking technological approach that emulates the biological navigation systems found in mammalian brains. This innovative method leverages experiences within cognitive space to plan global paths to targets, showcasing remarkable autonomy and adaptability to various environments. This work introduces a novel bionic, goal-oriented path planning approach for mobile robots. First, an experience map is constructed using NeuroSLAM, a bio-inspired simultaneous localization and mapping method. Based on this experience map, a successor representation model is then developed through reinforcement learning, and a goal-oriented predictive map is formulated to address long-term reward estimation challenges. By integrating goal-oriented rewards, the proposed algorithm efficiently plans optimal global paths in complex environments for mobile robots. Our experimental validation demonstrates the method’s effectiveness in experience sequence prediction and goal-oriented global path planning. The comparative results highlight its superior performance over traditional Dijkstra’s algorithm, particularly in terms of adaptability to environmental changes and computational efficiency in optimal global path generation. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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14 pages, 7905 KB  
Article
A Miniature Jumping Robot Using Froghopper’s Direction-Changing Concept
by Dong-Jun Lee and Gwang-Pil Jung
Biomimetics 2025, 10(5), 264; https://doi.org/10.3390/biomimetics10050264 - 24 Apr 2025
Viewed by 740
Abstract
To improve the maneuverability and agility of jumping robots, a variety of steerable jumping mechanisms have been actively studied. The steering ability enables a robot to reach a particular target by altering its jumping direction. To make this possible, we propose a miniature [...] Read more.
To improve the maneuverability and agility of jumping robots, a variety of steerable jumping mechanisms have been actively studied. The steering ability enables a robot to reach a particular target by altering its jumping direction. To make this possible, we propose a miniature steerable jumping robot based on froghopper’s jumping principle: Moment cancellation is achieved via synchronous leg rotation, and a predictable jumping direction is achieved through an almost zero stiffness femoro-tibial joint. To satisfy these working principles, the robot is designed to have a four-bar shaped body structure and wire-driven knee joints. The four-bar body always synchronizes the leg operation by mechanically coupling the two jumping legs, which enables the robot to cancel out the moments and finally reduce the needless body spin. The knee joints are actuated using wires, and the wires are kept loose to maintain joint stiffness almost zero during take-off. Accordingly, the jumping direction is successfully predicted to determine the initial posture of the tibia. As a result, the proposed robot can change the jumping direction from −20 degrees to 20 degrees while reducing needless body spin. Full article
(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)
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