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Biomimetics
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Bionic Intelligent Robots
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Bionic Intelligent Robots

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1768

Special Issue Editors

Dr. Yongbin Sun

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China
Interests: bionic intelligent control; bionic swarm control; bionic intelligent perception
Dr. Daifeng Zhang

E-Mail Website
Guest Editor
Institute of Automation, Qilu University of Technology, Jinan 250014, China
Interests: bionic swarm intelligence; control and planning for UAVs and UGVs; multi-agent system
Dr. Wanying Ruan

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Interests: bionic swarm intelligence; swarm intelligence game confrontation; UAV autonomous control

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, entitled “Bionic Intelligent Robots” in the journal Biomimetics. Novel and innovative solution approaches for robots are provided by simulating the excellent structural and information-processing characteristics of natural organisms. Over the last decade, this research area has blossomed, with significant efforts devoted to the research and development of bionic intelligent robot systems. However, many issues remain to be explored, discovered, and understood. Therefore, we propose this Special Issue on “Bionic Intelligent Robots” to provide a platform to exhibit the state of the art in this field.

This Special Issue is devoted to collecting the latest developments and achievements in the study of bionic intelligent robot systems, and encourages researchers to participate in this promising and challenging research area. For this purpose, papers focusing on new methods and applications of bionic intelligent robots are welcomed, including, but not limited to, the following topics:

  • Bionic mechanism modeling;
  • Bionic robot motion mechanism;
  • Bionic robot structural design and optimization;
  • Bionic robot multimodal perception;
  • Bionic robot autonomous control;
  • Bionic movement and behavior control for robots;
  • Bionic robot embodied intelligence;
  • Cross-domain bionic robot;
  • Bionic intelligence optimization;
  • Bionic swarm strategy and optimization for robots;
  • Bionic swarm control theories and technologies for robots;
  • Distributed consensus for robots and applications;
  • Bionic swarm control of heterogeneous teams (combining manned/unmanned systems or different vehicles).

We look forward to receiving your contributions to this Special Issue and to working together to advance the field of bionic intelligent robots.

Dr. Yongbin Sun
Dr. Daifeng Zhang
Dr. Wanying Ruan
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

  • bionic mechanism
  • bionic robot design
  • bionic multimodal perception
  • bionic autonomous control
  • bionic embodied intelligence
  • cross-domain bionic robot
  • bionic intelligence optimization
  • bionic swarm control
  • bionic swarm intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

24 pages, 2617 KB  
Article
Pigeon-Inspired Depth-Reasoning-Driven Decision Framework for Autonomous Traversal Flight of Quadrotors in Unmapped 3D Spaces
by Yongbin Sun and Rongmao Su
Biomimetics 2026, 11(4), 283; https://doi.org/10.3390/biomimetics11040283 - 19 Apr 2026
Viewed by 443
Abstract
Autonomous traversal flight in unknown 3D environments remains challenging due to mapping bottlenecks and computational latency. Inspired by pigeons navigating cluttered forests through instantaneous visual perception rather than constructing global metric maps, this paper presents a pigeon-inspired depth-reasoning-driven decision framework for agile quadrotor [...] Read more.
Autonomous traversal flight in unknown 3D environments remains challenging due to mapping bottlenecks and computational latency. Inspired by pigeons navigating cluttered forests through instantaneous visual perception rather than constructing global metric maps, this paper presents a pigeon-inspired depth-reasoning-driven decision framework for agile quadrotor traversal in unmapped spaces without explicit map construction. To ensure feasibility, we leverage a robust state estimation backbone enhanced by deep-learning-based feature matching, providing stable pose feedback under aggressive maneuvers. The core contribution is a pigeon-inspired depth-reasoning framework that translates raw sensory depth data into a hybrid optimization framework, integrating both hard safety constraints and soft geometric smoothness constraints, directly emulating the three avian mechanisms: gap selection via instantaneous depth gradients, path selection that minimizes posture changes, and a safety field driven by the looming effect. By bypassing time-consuming mapping and spatial discretization processes, the framework significantly reduces perception-to-control latency. Finally, validated via simulations and real-world experiments on a resource-constrained quadrotor platform, our map-less approach achieves superior decision frequencies and comparable safety margins to those of state-of-the-art map-based planners. This framework offers a practical, high-frequency solution for autonomous flight where computational resources and environmental knowledge are strictly limited. Full article
(This article belongs to the Special Issue Bionic Intelligent Robots)
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20 pages, 33271 KB  
Article
An Error-Adaptive Competition-Based Inverse Kinematics Approach for Bimanual Trajectory Tracking of Humanoid Upper-Limb Robots
by Jiaxiu Liu, Zijian Wang, Hongfu Tang, Hongzhe Jin and Jie Zhao
Biomimetics 2026, 11(4), 279; https://doi.org/10.3390/biomimetics11040279 - 17 Apr 2026
Viewed by 367
Abstract
Humanoid upper-limb robots are an important direction in biomimetic robotics, and inverse kinematics is a key technique for achieving human-like coordinated operation. However, existing inverse kinematics methods for bimanual trajectory tracking often suffer from high computational complexity and limited synchronization performance. To address [...] Read more.
Humanoid upper-limb robots are an important direction in biomimetic robotics, and inverse kinematics is a key technique for achieving human-like coordinated operation. However, existing inverse kinematics methods for bimanual trajectory tracking often suffer from high computational complexity and limited synchronization performance. To address this, this paper proposes an error-adaptive competition-based inverse kinematics (EAC-IK) approach for bimanual trajectory tracking of humanoid upper-limb robots. First, a unified modeling framework for the absolute tracking errors and synchronization errors of the two arms is established, and the end-effector task constraints are reformulated into a low-dimensional representation, thereby reducing the computational complexity of the original high-dimensional task mapping. Second, to enhance the coordination capability of bimanual operations, an error-adaptive competition mechanism is developed to regulate the weighting coefficients of the two arms online according to their error states. In addition, a virtual second-order command shaper is introduced at the joint level to reconstruct joint trajectories and suppress oscillations induced by input noise and the error-adaptive competition mechanism. Simulation and experimental results on a hyper-redundant humanoid upper-limb robot demonstrate that, compared with the zeroing neural-network-based inverse kinematics method, the proposed method achieves lower tracking and synchronization errors, as well as higher computational efficiency. In the circular trajectory-tracking experiment, the left-arm position and orientation tracking errors decrease from 1.60×10−3m and 4.72×10−3rad to 0.70×10−3m and 0.95×10−3rad, respectively, while the synchronization error decreases from 1.96×10−3 to 1.30×10−3. In addition, the average algorithm runtime decreases from 0.82ms to 0.63ms. Full article
(This article belongs to the Special Issue Bionic Intelligent Robots)
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24 pages, 21933 KB  
Article
Parametrized Graph Convolutional Multi-Agent Reinforcement Learning with Hybrid Action Spaces in Dynamic Topologies
by Pei Chi, Chen Liu, Jiang Zhao and Yingxun Wang
Biomimetics 2026, 11(4), 232; https://doi.org/10.3390/biomimetics11040232 - 1 Apr 2026
Viewed by 590
Abstract
Multi-agent swarm collaboration, inspired by the collective behaviors of biological swarms in nature, has wide applications in dynamic open environments. However, hybrid action spaces in multi-agent reinforcement learning (MARL) present a critical challenge: the inherent coupling between discrete and continuous actions severely undermines [...] Read more.
Multi-agent swarm collaboration, inspired by the collective behaviors of biological swarms in nature, has wide applications in dynamic open environments. However, hybrid action spaces in multi-agent reinforcement learning (MARL) present a critical challenge: the inherent coupling between discrete and continuous actions severely undermines policy stability and convergence, especially under dynamic topologies. Existing methods fail to decouple this coupling, leading to suboptimal policies and unstable training. This paper addresses the core problem of action coupling under dynamic topologies, proposing a Parametrized Graph Convolution Reinforcement Learning (P-DGN) method. Operating within the actor–critic framework, P-DGN decouples the optimization pathways for hybrid actions, with a biomimetic observation design inspired by starling flock behaviors: each agent only observes the states of its seven nearest neighbors to achieve efficient local interaction and global collaboration. Its actor network uses multi-head attention to build dynamic relation kernels, develops temporal relation regularization (TRR) to improve policy consistency across time steps, and generates continuous actions with a Gaussian policy. Meanwhile, P-DGN’s critic network, based on deep Q-network (DQN), evaluates Q-values for discrete actions to guide optimal choices. We evaluate P-DGN in two different multi-agent cooperative environments. Experimental results show that compared with parametrized deep Q-network (P-DQN) and DQN baseline, the proposed method has faster convergence speed and stronger training stability. Moreover, with dense rewards, P-DGN agents learn emergent tactics like encirclement. Overall, P-DGN offers a new approach for optimizing hybrid action spaces in multi-agent systems within open, dynamic environments, balancing theoretical generality with practical utility, and its biomimetic design provides a biologically plausible framework for multi-agent swarm collaboration. Full article
(This article belongs to the Special Issue Bionic Intelligent Robots)
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