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Automatic Control of Magnetic Helical Microrobots Docking with Target Objects in Liquid Environments

Journal of Bionic Engineering 2025年第2期22卷 574-584页

作者： Fu Zhao Haoran Rong Lefeng Wang State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin150080China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin150080China

Bio-inspired magnetic helical microrobots have great potential for biomedical and micromanipulation applications. Precise interaction with objects in liquid environments is an important prerequisite and challenge for helical microrobots to perform various tasks. In this study, an automatic control method is proposed to realize the axial docking of helical microrobots with arbitrarily placed cylindrical objects in liquid environments. The docking process is divided into ascent, approach, alignment, and insertion stages. First, a 3D docking path is planned according to the positions and orientations of the microrobot and the target object. Second, a steering-based 3D path-following controller guides the helical microrobot to rise away from the container bottom and approach the target along the path. Third, based on path design with gravity compensation and steering output limits, alignment of position and orientation can be accomplished simultaneously. Finally, the helical microrobot completes the docking under the rotating magnetic field along the target orientation. Experiments verified the automatic docking of the helical microrobot with static targets, including connecting with micro-shafts and inserting into micro-tubes. The object grasping of a reconfigurable helical microrobot aided by 3D automatic docking was also demonstrated. This method enables precise docking of helical microrobots with objects, which might be used for capture and sampling, in vivo navigation control, and functional assembly of microrobots.

关键词： Magnetic actuation Helical microrobots Visual servo Motion control Docking

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Integrated Cross-Scale Manipulation and Modulable Encapsulation of Cell-Laden Hydrogel for Constructing Tissue-Mimicking Microstructures

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Research 2025年第1期2024卷 704-717页

作者： Yanfeng Zhao Xinyi Dong Yang Li Juan Cui Qing Shi Hen-Wei Huang Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Peking University First Hospital Xicheng DistrictBeijing 100034China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of EducationNorth University of ChinaTaiyuan 030051China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Laboratory for Translational Engineering Harvard Medical SchoolCambridgeMA 02139USA Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of EducationBeijing 100081China

Engineered microstructures that mimic in vivo tissues have demonstrated great potential for applications in regenerative medicine,drug screening,and cell behavior ***,current methods for engineering microstructures that mimic the multi-extracellular matrix and multicellular features of natural tissues to realize tissue-mimicking microstructures in vitro remain ***,we propose a versatile method for constructing tissue-mimicking heterogeneous microstructures by orderly integration of macroscopic hydrogel exchange,microscopic cell manipulation,and encapsulation ***,various cell-laden hydrogel droplets are manipulated at the millimeter scale using electrowetting on dielectric to achieve efficient hydrogel ***,the cells are manipulated at the micrometer scale using dielectrophoresis to adjust their density and arrangement within the hydrogel ***,the photopolymerization of these hydrogel droplets is triggered in designated regions by dynamically modulating the shape and position of the excitation ultraviolet ***,heterogeneous microstructures with different extracellular matrix geometries and components were constructed,including specific cell densities and *** resulting heterogeneous microstructure supported long-term culture of hepatocytes and fibroblasts with high cell viability(over 90%).Moreover,the density and distribution of the 2 cell types had significant effects on the cell proliferation and urea *** propose that our method can lead to the construction of additional biomimetic heterogeneous microstructures with unprecedented potential for use in future tissue engineering applications.

关键词： cell behavior explorationhowevercurrent tissue mimicking microstructures cell laden hydrogel electrowetting dielectric regenerative medicinedrug screeningand heterogeneous microstructures engineered microstructures orderly integration

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Evolution Design Methodology of the Metamorphic Mechanisms Inspired by Gene Deduction

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Journal of Mechanical Design 2025年第8期147卷 083302页

作者： Zhang, Yang Xu, Peng Li, Bing Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robots Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong School of Robotics and Advanced Manufacture Harbin Institute of Technology Shenzhen 518052 China

This article proposes an evolutionary design method for metamorphic mechanisms inspired by gene deduction. First, the correlations between biological evolution and mechanical evolution, biological systems and mechanical systems, and genes and mechanisms are revealed. There are many similarities between biology and mechanism. Biological principles can be applied to the study and design of the mechanisms to adapt well to tasks and environments. The correlation between genetic operation and mechanical design is developed. The selection operator, recombination operator, and mutation operator of the mechanisms are deduced by inspiring genes. To verify the rationality of these operators, they are used to construct 5R (revolute joints) overconstrained closed-loop linkage, 6R metamorphic closed-loop linkage, 7R deployable closed-loop linkage, and 2RP (prismatic joint) RR metamorphic parallel mechanism (MPM). The mobilities of these mechanisms are analyzed via the atlas method. Finally, the evolutionary design method of MPMs is introduced and used to design the mechanisms with 2R1T (two rotation one translation) and 1R2T modes, which verify the effectiveness of the method. The proposed evolutionary design method provides a new theoretical tool for the design of metamorphic single-loop and parallel mechanisms.

关键词： bio-inspired design Chain Design methodology evolution design Design Parallel mechanisms Kinematics metamorphic mechanisms single-loop/parallel mechanisms Kinematic chains gene deduction atlas method Biology Bifurcation

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Dual-modal Motion of Magnetically Controlled Soft Microrobots with Environmentally Responsive Reversible Deformation 24

Dual-modal Motion of Magnetically Controlled Soft Microrobot...

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2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Xin, Zhengyuan Wu, Anping Bai, Kailun Yang, Haotian Lin, Kaijun Shi, Qing Wang, Huaping Hou, Yaozhen Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400710681

Soft microrobots have garnered attention in the biomedical field due to their ability to access hard-to-reach regions within living organisms for precise manipulation. However, the inability to alter the stable morphology after design and fabrication remains a pressing issue for existing soft microrobots. This limitation results in a fixed range of locomotion modes, thereby restricting the diversity of operational environments. Addressing this challenge, this study proposes and designs a biocompatible X-shaped soft microrobot that achieves alterable stable morphologies through an innovative mechanism. We developed a deformable soft hydrogel robot based on alginate gel’s ability to reversibly swell in response to ion concentration changes. Based on the deformation characteristics of this microrobot, we have designed undulating and rolling locomotion modes that are suitable for different morphologies, along with their corresponding magnetic field manipulation systems, enabling multi-morphology locomotion of the robot. Experimental validations demonstrate that the designed flexible microrobot can achieve a deformation deflection angle of up to 150° under manipulation. This robot not only performs forward undulating locomotion in its expanded state but also executes rolling motion in its spherical configuration, exhibiting high locomotion flexibility and adaptability. © 2024 Copyright held by the owner/author(s).

关键词： Microrobots

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Formation Control of Unmanned Underwater Vehicles With Unknown Absolute Position Using Rigid Graph-Based MPC

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IEEE Internet of Things Journal 2025年

作者： Fu, Bowen Zhu, Daqi Chen, Mingzhi Yang, Simon X. University of Shanghai for Science and Technology Shanghai200093 China University of Guelph Advanced Robotics and Intelligent Systems Laboratory School of Engineering GuelphONN1G2W1 Canada

Formation control of unmanned underwater vehicles (UUVs) is of great significance to the Internet of Underwater Things (IoUT). Currently, various methods are employed for UUV formation control. Among them, rigid graph-based approaches, which rely solely on relative information between UUVs, are particularly suitable for underwater environments. However, traditional rigid graph-based formation control methods often face challenges such as control jumps and weak robustness in dynamic underwater environments. To address these challenges, a novel rigid graph-based model predictive controller (RGMPC) is proposed in this paper. The proposed approach integrates model predictive control with traditional rigid graph-based methods, effectively avoiding issues such as control jumps and thrust saturation while enhancing robustness. Moreover, constraints are constructed using backstepping methods to theoretically ensure the closed-loop stability of the algorithm. Finally, the algorithm is validated through extensive simulations based on mathematical models and further tested in the Gazebo simulator, fully demonstrating its feasibility and effectiveness. © 2014 IEEE.

关键词： Predictive control systems

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Comprehensive Performance Optimization of a Novel Anthropomorphic Dexterous Hand

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IEEE robotics and Automation Letters 2025年第7期10卷 6816-6823页

作者： Yifan Ma Weiwei Shang Fei Zhang Shunxiang Pang Shuqing Dai Zhengkun Yi Guotao Li Shuang Cong Department of Automation University of Science and Technology of China Hefei China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions.

关键词： Thumb Hands Kinematics Grasping Force Performance analysis IP networks Electronics packaging Cables Accuracy

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Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment

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Frontiers of Information Technology & Electronic Engineering 2019年第3期20卷 330-341页

作者： Da-qi ZHU Yun QU Simon X.YANG Laboratory of Underwater Vehicles and Intelligent Systems Shanghai Maritime University Advanced Robotics and Intelligent Systems Laboratory University of Guelph

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles(AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

关键词： Autonomous underwater vehicles Self-organizing neural networks Azimuths Ocean current

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Obstacle-Avoidance Motion Planning Method of Manipulator Based on Deep Reinforcement Learning

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Unmanned systems 2025年

作者： Liu, Yiyang Wu, Defan Fu, Yukai Bai, Hongfei Deng, Chao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

In recent years, driven by the rapid advancement of industrial automation and intelligent manufacturing, the demand for manipulators to perform tasks in complex environments has significantly increased. Traditional motion planning methods face challenges of computational complexity and lack of adaptability when handling robots with redundant degrees of freedom. To address these issues, this paper proposes a novel obstacle avoidance motion planning method for manipulators based on deep reinforcement learning (DRL), which is especially important when unmanned systems operate in a GPS-free environment. The primary contributions are twofold: (i) we propose an innovative algorithm, termed PPO-HER, which incorporates hindsight experience replay (HER) into proximal policy optimization (PPO) during training, effectively leveraging failed experiences to enhance policy learning efficiency;(ii) an adaptive action exploration strategy is designed to accelerate training convergence and avoid local optima. Experimental results demonstrate that the PPO-HER algorithm and the adaptive action exploration strategy perform exceptionally well across three environments of varying difficulty, significantly improving network training speed and success rates, shortening planned paths, and enhancing autonomous obstacle avoidance capabilities. These findings provide new ideas for target location and tracking of unmanned systems, and lay the foundation for future learning and data-driven approaches in complex environments. © World Scientific Publishing Company.

关键词： Deep reinforcement learning

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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PTRL: Prior Transfer Deep Reinforcement Learning for Legged Robots Locomotion

arXiv

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arXiv 2025年

作者： Huang, Haodong Sun, Shilong Zhao, Zida Huang, Hailin Shen, Changqing Xu, Wenfu School of Robotics and Advanced Manufacturing Harbin Institute of Technology Shenzhen518055 China School of Rail Transportation Soochow University Suzhou215006 China Suzhou Boyata Industrial Internet Company Ltd. Suzhou215100 China Key University Laboratory of Mechanism and Machine Theory and Intelligent Unmanned Systems of Guangdong Guangdong Shenzhen518055 China

In the field of legged robot motion control, reinforcement learning (RL) holds great promise but faces two major challenges: high computational cost for training individual robots and poor generalization of trained models. To address these problems, this paper proposes a novel framework called Prior Transfer Reinforcement Learning (PTRL), which improves both training efficiency and model transferability across different robots. Drawing inspiration from model transfer techniques in deep learning, PTRL introduces a fine-tuning mechanism that selectively freezes layers of the policy network during transfer, making it the first to apply such a method in RL. The framework consists of three stages: pre-training on a source robot using the Proximal Policy Optimization (PPO) algorithm, transferring the learned policy to a target robot, and fine-tuning with partial network freezing. Extensive experiments on various robot platforms confirm that this approach significantly reduces training time while maintaining or even improving performance. Moreover, the study quantitatively analyzes how the ratio of frozen layers affects transfer results, providing valuable insights into optimizing the process. The experimental outcomes show that PTRL achieves better walking control performance and demonstrates strong generalization and adaptability, offering a promising solution for efficient and scalable RL-based control of legged robots. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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