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Lidong Yang

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12 papers

2 author rows

EAAI Journal 2026 Journal Article

Multi-grained detail-enhanced and patch-aware network based on bird sound recognition

Lin Duan
Lidong Yang
Dawei Niu
Yong Guo
Yu Gu

Combining deep learning and bird sound recognition strongly supports monitoring bird species and maintaining ecological balance. However, in outdoor environments, the extraction of bird sound features is often hindered by environmental noise, making it challenging for models to learn the fine-grained features of bird sounds fully. And single-scale feature extraction is harrowing to cover the time–frequency domain feature information of bird sounds in multiple dimensions. To address these issues, this paper proposes a multi-grained detail-enhanced and patch-aware network. The model utilizes densely connected time delay neural network as the backbone network and introduces the multi-grained detail-enhanced convolution, which combines vanilla convolutions with differential convolutions in the horizontal, vertical, angular, and central levels, and incorporates multi-grained pooling strategies to learn fine-grained acoustic features at different levels. To further overcome the limitations of single-scale feature extraction, the branch patch-aware attention module is proposed. This module collaboratively captures local details and global contextual information through a multi-branch structure and patch partitioning of different sizes. On the three datasets, the method achieved accuracies of 96. 29%, 86. 51%, and 97. 40%, respectively. This achievement demonstrates the precise capture and parsing ability of the method for audio feature information.

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IROS Conference 2025 Conference Paper

Learning-Based Motion Controller for Reconfigurable Microswarms

Yamei Li
Yunxi Tang
Yun Wang
Yangmin Li 0001
Lidong Yang

Motion control of magnetic microswarms has attracted extensive attention due to its significance in microrobots-based biomedical applications such as targeted drug delivery. However, such reconfigurable microswarms are subject to complex interactions between individuals and environments which make accurate modeling challenging. These complexities of microswarms poses challenges for precise motion control, as traditional controllers often rely on precise mathematical models and manual parameter tuning that limits their scalability and efficiency. Learning-based methods, such as Deep Reinforcement Learning (DRL), offer an alternative but require large datasets (usually on the order of millions) and extensive exploration which may cause the microswarms instability in physical environments due to unreasonable actions during early training therefore results in the sim-to-real gap. Moreover, traditional DRL focuses on instantaneous state-action mappings, neglecting the sequential dependencies critical for accurate motion control, leading to low tracking accuracy in complex scenarios. To address these challenges, we propose a Learning from Demonstration (LfD)-based motion control framework, which inherently encode compensatory behaviors and task-specific adaptability into neural networks, enabling adaptive performance even under unmodeled disturbances. Furthermore, the neural networks consider a time series of microswarm states to determine the future control actions, enabling the system to learn sequential dependencies and transitions between states so as to ensure smooth and accurate motion control. Simulations and comparative experiments validate our framework’s effectiveness and demonstrate superior control accuracy and adaptability to microswarm’s shape changes.

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ICRA Conference 2023 Conference Paper

DQN-based on-line Path Planning Method for Automatic Navigation of Miniature Robots

Jialin Jiang
Lidong Yang
Li Zhang 0010

Untethered magnetic microrobots with control-lable locomotion property and multiple functions have attracted lots of attention in recent years. Owing to the small scale, micro-robots with automatic navigation possess a promising perspec-tive for biomedical applications including precise delivery and targeted therapy in confined and narrow space, especially for in-vivo scenario. However, the practical working environment for microrobots can be various, dynamic, and complicated, and path planning algorithm applicable for both dynamic obstacle avoidance and planning in maze-like environments still remains a challenge. Furthermore, considering the sizes, different types of microrobots may occupy different proportions of the field of vision. The safe distance between the waypoints and the obstacles needs to be taken into thoughts. In this work, we proposed a reinforcement learning-based strategy capable of real-time path planning for microrobots in different scales. The reference moving direction at each control period is provided by a deep Q network (DQN) according to the local surrounding environment, and the corresponding control magnetic field is generated via a 3-axis Helmholtz coil system. A distur-bance observer (DOB) is responsible for the locomotion state observation and direction error compensation. Experiments demonstrate the effectiveness of our proposed strategy using microrobots with different locomotion mechanisms and scales, in both virtual dynamic obstacle environments and channel-like environments.

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ICRA Conference 2023 Conference Paper

QuadMag: A Mobile-Coil System With Enhanced Magnetic Actuation Efficiency and Dexterity

Lidong Yang
Moqiu Zhang
Zhengxin Yang
Haojin Yang 0002
Li Zhang 0010

Magnetic field is a favorable power source for actuation and control of micro-/nanorobots. To overcome the fast decay of magnetic field for large-workspace microrobotic actuation, mobile field source-based systems have been proposed. In this work, we report a new mobile-coil system, i. e. , QuadMag. It consists of four electromagnetic coils, whose motion is actuated by a parallel mechanism. Compared to previous systems with three mobile coils, e. g. , DeltaMag, the additional coil in the QuadMag increases the degree-of-freedom (DoF) for magnetic control. However, to control QuadMag, new control methods should be developed for the over-constrained parallel mechanism and for the field/force of the four coils. We derive the Jacobian matrix for the differential motion of the parallel mechanism and then formulate the field, force and simultaneous field and force control methods for magnetic actuation. Comparative experiments validate the enhanced actuation efficiency when controlling torque-driven helical microrobots. Moreover, the magnetic actuation dexterity is also enhanced by the additional coil. We conduct simulated navigation experiments and prove the actuation capability of QuadMag for 3D force-driven microrobot navigation with controlled robot orientation.

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IROS Conference 2022 Conference Paper

Torque-Actuated Multimodal Locomotion of Ferrofluid Robot With Environment and Task Adaptability

Lidong Yang
Mengmeng Sun
Li Zhang 0010

Soft microrobotics have recently been an active field that advances microrobotics with new robot design, locomotion, and applications. In this paper, we study the ferrofluid robot (FR), which has soft nature and exhibits paramagnetism. Currently, the FR locomotion is usually realized by magnetic force. To enable the FR with more locomotion modes for environment and task adaptability, we program three dynamic field forms and realize three corresponding torque-actuated locomotion modes: Rolling, Wobbling, and Oscillating. The torque actuation of the FR is formulated, and the three locomotion modes are characterized. With the implementation of automated tracking and control algorithms, the controllability of these modes is testified. We then fabricate different environments to validate the adaptability of the FR that can switch its locomotion mode accordingly. Finally, utilizing the oscillating mode and wobbling mode, we demonstrate the transport of lipophilic and hydrophilic cargoes, respectively, showing the task adaptability.

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IROS Conference 2021 Conference Paper

Hybrid Magnetic Force and Torque Actuation of Miniature Helical Robots Using Mobile Coils to Accelerate Blood Clot Removal

Lidong Yang
Moqiu Zhang
Haojin Yang 0002
Zhengxin Yang
Li Zhang 0010

Mechanical rubbing of blood clot using miniature magnetic helical robots is a potential way for thrombolysis. In this paper, we report a new strategy for this issue based on mobile coils. Previously, we proposed the concept of magnetic actuation with parallel mobile coils, in which multiple coils can move in 3D space. Enabled by mobility of the coils, additional degree-of-freedom (DOF) could be utilized for actuation performance optimization. Besides the primary helical propulsion by rotating magnetic fields, our strategy aims to optimize the coil motion to make the magnetic force contributes the most to the helical robot forward motion. For this goal, modeling of the magnetic field and force of multiple mobile coils are presented, based on which an optimization algorithm is formulated to output the best coil motion. For validation, an enhanced mobile coil system having a workspace of Φ500 mm ×150 mm is constructed based on the parallel mobile coil concept. Simulations show the effectiveness of the proposed strategy, whose effective workspace for a specific task can also be obtained. After implementing the proposed strategy, preliminary experiments using clot analog demonstrate that the removal speed is accelerated over 50% compared to that without coil motion optimization.

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IROS Conference 2021 Conference Paper

Simultaneous Actuation and Localization of Magnetic Robots Using Mobile Coils and Eye-In-Hand Hall-Effect Sensors

Moqiu Zhang
Lidong Yang
Chong Zhang
Zhengxin Yang
Li Zhang 0010

Large workspace localization of magnetic robots is important for medical applications. This paper presents a novel localization strategy to achieve simultaneous localization and actuation of magnetic robots using hall-effect sensors. We integrate 25 sensors into a sensing probe and mount it on to the mobile-coil system, which realizes accurate sensing and actuation of magnetic devices within a cylindrical workspace of ϕ500 mm×150 mm. Simulation results show the average localization error using the proposed method is 1. 7 mm. A verification experiment is conducted to prove the design advantages; Another two experiments are conducted to demonstrate the simultaneous actuation and localization of a torque-driven robot and a force-driven floating robot respectively. For the force-driven floating robot, the average variation between the localization results and the desired trajectory is less than 2 mm.

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ICRA Conference 2020 Conference Paper

A Mobile Paramagnetic Nanoparticle Swarm with Automatic Shape Deformation Control

Lidong Yang
Jiangfan Yu
Li Zhang 0010

Recently, swarm control of micro-/nanorobots has drawn much attention in the field of microrobotics. This paper reports a mobile paramagnetic nanoparticle swarm with the capability of active shape deformation that can improve its environment adaptability. We show that, by applying elliptical rotating magnetic fields, a swarm pattern called the elliptical paramagnetic nanoparticle swarm (EPNS) would be formed. When changing the field ratio-α (i. e. the strength ratio between the minor axis and major axis of the elliptical field), the shape ratio-β of the EPNS (i. e. the length ratio between the major axis and minor axis) will change accordingly. However, automatically control this shape deformation process has difficulties because the deformation dynamics has strong nonlinearity, model variation and long time requirement. To solve this problem, we propose a fuzzy logic-based control scheme that utilizes the knowledge and control experience from skilled human operators. Experiments show that the proposed control scheme can stably maneuver the shape deformation of the EPNS with small overshoot, which cannot be achieved by conventional PI control. Moreover, experimental results show that, with the automatic shape deformation control, shape of the EPNS is controlled with high reversibility and also can be well maintained during the planar rotational and translational locomotion of the EPNS.

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ICRA Conference 2020 Conference Paper

Eye-in-Hand 3D Visual Servoing of Helical Swimmers Using Parallel Mobile Coils

Zhengxin Yang
Lidong Yang
Li Zhang 0010

Magnetic helical microswimmers can be propelled by rotating magnetic field and are adept at passing through narrow space. To date, various magnetic actuation systems and control methods have been developed to drive these microswimmers. However, steering their spacial movement in a large workspace is still challenging, which could be significant for potential medical applications. In this regard, this paper designs an eye-in-hand stereo-vision module and corresponding refraction-rectified location algorithm. Combined with the motor module and the coil module, the mobile-coil system is capable of generating dynamic magnetic fields in a large 3D workspace. Based on the system, a robust triple-loop stereo visual servoing strategy is proposed that operates simultaneous tracking, locating, and steering, through which the helical swimmer is able to follow a long-distance 3D path. A scaled-up magnetic helical swimmer is employed in the path following experiment. Our prototype system reaches a cylindrical workspace with a diameter more than 200 mm, and the mean error of path tracking is less than 2 mm.

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ICRA Conference 2019 Conference Paper

DeltaMag: An Electromagnetic Manipulation System with Parallel Mobile Coils

Lidong Yang
Xingzhou Du
Edwin Yu
Dongdong Jin
Li Zhang 0010

In this paper, a novel magnetic manipulation system using mobile coils for remote actuation of magnetic untethered devices in an enlarged workspace is proposed and studied. A parallel mechanism is implemented to actuate the mobile coils. A proof-of-concept prototype is designed and constructed, namely the DeltaMag, which includes three electromagnetic coils for generating magnetic fields and three motors for actuation of the coils. It has good space utilization: ratio between the diameter of the workspace and the diameter of the whole prototype reaches 0. 7. A calibrated mathematical model is developed for the field distribution of a single coil, which has an average error of 8. 75%. Then, we introduce a calculation method for the 3D magnetic field at any working position for the configuration of multiple parallel mobile coils. Moreover, an embedded system is established for actuating the parallel mechanism, whose pose is fed back via serial communication for magnetic field computation. A vision based approach is developed for closed-loop control of the parallel mechanism. Furthermore, experiments demonstrate the capabilities of the DeltaMag for manipulation of a magnetic catheter mock-up and a magnetic capsule mock-up in a workspace with a diameter more than 200 mm.

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IROS Conference 2018 Conference Paper

Automated Control of Multifunctional Magnetic Spores Using Fluorescence Imaging for Microrobotic Cargo Delivery

Lidong Yang
Yabin Zhang 0007
Chi-Ian Vong
Li Zhang 0010

Microrobotic cargo delivery possesses promising perspective for precision medicine, and has attracted much attention recently. However, its automation remains challenging, especially with complex environmental conditions, such as obstacles and obstructed optical feedback. In this paper, we propose an automated control approach for a new microrobotic cargo carrier, i. e. the multifunctional magnetic spore (Mag-Spore). By surface functionalization of the spore with Fe 3 O4 nanoparticles and carbon quantum dots, it can be remotely actuated and tracked by an electromagnetic coil system and the fluorescence microscopy, respectively. Our strategy utilizes fluorescence imaging for vision feedback, which enhances the recognition and tracking of Mag-Spores and cells. Then, information of the cells and Mag-Spores for planning and control is identified via image processing, and an optimal path planner with obstacle avoidance capability is designed based on the Particle Swarm Optimization (PSO)algorithm. To make the Mag-Spore follow the planed path accurately, an observer-based trajectory tracking controller is synthesized. Simulations and experiments are conducted to demonstrate the effectiveness of the proposed control approach.

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IROS Conference 2018 Conference Paper

Magnetic Navigation of a Rotating Colloidal Swarm Using Ultrasound Images

Qianqian Wang 0003
Lidong Yang
Jiangfan Yu
Chi-Ian Vong
Philip Wai Yan Chiu
Li Zhang 0010

Microrobots are considered as promising tools for biomedical applications. However, the imaging of them becomes challenges in order to be further applied on in vivo environments. Here we report the magnetic navigation of a paramagnetic nanoparticle-based swarm using ultrasound images. The swarm can be generated using simple rotating magnetic fields, resulting in a region containing particles with a high area density. Ultrasound images of the swarm shows a periodic changing of imaging contrast. The reason for such dynamic contrast has been analyzed and experimental results are presented. Moreover, this swarm exhibits enhanced ultrasound imaging in comparison to that formed by individual nanoparticles with a low area density, and the relationship between imaging contrast and area density is testified. Furthermore, the microrobotic swarm can be navigated near a solid surface at different velocities, and the imaging contrast show negligible changes. This method allows us to localize and navigate a microrobotic swarm with enhanced ultrasound imaging indicating a promising approach for imaging of microrobots.

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