Microswarms face challenges in precise delivery within dynamic biological fluids due to fluid disturbances and limited operational intuitiveness. Current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities, particularly in complex tasks that require a balance between flexibility and precision. In this study, we propose a haptic-assisted magnetic actuation control strategy, establishing a human-in-the-loop control framework. The haptic perception system provides the operator with haptic feedback reflecting the interactions between the microswarm and the environment. A real-time tracking system monitors the position and pattern of the controlled microswarm in remote environments, and transmits this information to the control system for decision-making. After characterizing the magnetic field parameters and magnetic nanoparticles, we have achieved real-time navigation and morphology modulation of the microswarm in dynamic flow conditions and three-dimensional (3D) space. Comparative experiments under various flow rate conditions demonstrate that the haptic-assisted strategy enhances microswarm control stability and precision across different flow regimes. Moreover, the human-machine collaboration mechanism improves delivery success rates (97%) under sudden disturbances compared to preprogrammed automated control and purely manual control, validating its potential for applications in complex biomedical scenarios. Our work provides a haptic-assisted microswarm control method in dynamic conditions, expanding an adaptive microswarm control strategy in complex biomedical environments.

Conventional Timed Elastic Band (TEB) algorithms often exhibit velocity fluctuations and generate unsmooth trajectories in complex environments, thereby compromising the motion efficiency and control stability of mobile robots. To address these limitations, this paper presents an Adaptive and Smooth TEB algorithm (AS-TEB) with self-regulatory capabilities. First, a curvature-based adaptive sampling strategy is developed, which reduces optimization dimensionality while preserving tracking accuracy and preliminarily enhancing trajectory and velocity continuity. Next, jerk constraints are incorporated to suppress high-frequency velocity fluctuations, thereby improving dynamic smoothness. Furthermore, a velocity-curvature radius constraint is introduced, explicitly linking linear velocity with path curvature and the robot’s minimum turning radius, enabling adaptive speed adjustment and alleviating velocity-curvature mismatches. Extensive simulations and hardware experiments demonstrate that AS-TEB significantly reduces velocity fluctuations, enhances trajectory smoothness, and maintains high motion efficiency compared to conventional TEB and several improved variants, exhibiting strong generality and stability on both differential and Ackermann platforms. This approach offers an efficient and practical solution for local path planning in complex dynamic environments.

Magnetic actuation is a promising approach in the robotic manipulation field, enabling wireless manipulation for small-scale operations. However, selective three-dimensional (3D) manipulation of multiple magnetic microrobots under global magnetic fields remains a challenge. This paper presents a dynamic magnetic modeling and vision-guided control strategy to realize 3D manipulation of magnetic microrobots, including patch-robot-assisted collective delivery and microrobot screening. An impedance regulation-based position control method is proposed, leveraging theoretical analysis of electromagnetic forces and fluid drag to accommodate microrobots with diverse morphologies. Through trajectory motion experiments, our control strategy ensures that the mean absolute errors (MAE) of the microrobots are consistently below 200 μm. By utilizing patch robot adhesion and differential magnetic responses among the microrobots, this strategy enables selective manipulation and collective sorting in a 3D space. Applications in patch-robot-assisted delivery, collective sorting and screening are validated. The proposed approach advances magnetic microrobot control by enabling spatially selective operations critical for biomedical tasks.

Swarming microrobots offer great promise for targeted delivery in biofluidic environments. However, current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities. This work proposes a real-time navigation and control strategy with haptic feedback for delivering magnetic microswarm, in which the haptic feedback system provides microswarm-environment interaction to the operator. The real-time tracking system continuously monitors the position and shape of the microswarm in the remote environment, transmitting data to the control system for decision-making. This integration can achieve real-time perception and feedback of the microswarm’s state and motion process. Moreover, the strategy successfully demonstrates navigation and shape-adaptive regulation of the microswarm under static, downstream and three-dimensional (3D) upstream flow conditions. The experimental results show that the haptic feedback enables real-time trajectory and velocity adjustments during navigation, improving control robustness and delivery accuracy. Our work expands a haptic feedback-enabled microswarm control in dynamic conditions, providing an adaptive swarm control strategy in complex biomedical environments.

Microrobotic swarms have shown promising features due to their collective and flexible behaviours, while achieving precise swarm control and autonomous navigation in complex environments remains a challenge. Here, we propose a Transformer-based reinforcement learning strategy that integrates Proximal Policy Optimization for autonomous swarm control in obstacle environments. By combining domain randomization, this strategy enables direct transfer from simulation to real-world without fine tuning. Experimental results demonstrate robust control performance in avoiding static obstacles and tracking the dynamic target, which is not validated in training. The swarm autonomously navigates and adjusts its velocity and trajectory in obstacle environments with an intact swarm pattern. Our work presents a scalable strategy for the deployment of microrobotic swarms with adaptive navigation capability through complex, constrained environments.

Collective microrobots enable controlled batch delivery, showing promising application in the biomedical field. However, significant challenges remain in achieving long-distance delivery of collective microrobots in dynamic environments. This study proposes a magnetic actuation strategy for delivering collective cell microrobots in flowing conditions. A magnetic actuation method is developed, and a mobile actuation system with multiple coils coordination is designed to generate spatially isotropic magnetic fields. Experiments of delivering collective microrobots are conducted in flowing conditions, including downstream and upstream with an average flow velocity up to 8.84 mm/s. Results demonstrate that the proposed actuation strategy enhances driving performance in dynamic environments, achieving long-distance delivery of collective microrobots (over 548 mm). The final access rate of microrobots reaches 90.63% and 94.79% in upstream and downstream conditions, respectively. Our strategy provides an efficient control method for delivering collective microrobots, showing potential for targeted delivery in biomedical applications.

Magnetic microrobots are showing great potential in micromanipulation due to the capability of motion control under external fields. However, achieving selective control of magnetic microrobots in three-dimensional (3D) space using global magnetic fields still presents a challenge. In this work, we propose a selective control strategy based on a movable electromagnetic coil system, incorporating a mass-spring-damping model to achieve precise control of cell microrobots in 3D space. By combining theoretical analysis with vision-based feedback, experiments are demonstrated in different scenarios, including step climbing and ring traversal, validating the control capability in different environments. Furthermore, by utilizing the differences in magnetic responses among cell microrobots, this strategy enables selective manipulation of multiple cell microrobots, demonstrating real-time sorting manipulation in a 3D space. Our work presents a strategy that can be applied to selectively manipulate magnetic microrobots in complex environments.

Soft magnetic miniature robots (SMMRs) have potential biomedical applications due to their flexible size and mobility to access confined environments. However, navigating the robot to a goal site with precise control performance and high repeatability in unstructured environments, especially in flow rate conditions, still remains a challenge. In this study, drawing inspiration from the control requirements of drug delivery and release to the goal lesion site in the presence of dynamic biofluids, we propose a flow rate rejection control strategy based on a deep reinforcement learning (DRL) framework to actuate an SMMR to achieve goal-reaching and hovering in fluidic tubes. To this end, an SMMR is first fabricated, which can be operated by an external magnetic field to realize its desired functionalities. Subsequently, a simulator is constructed based on neural networks to map the relationship between the applied magnetic field and robot locomotion states. With minimal prior knowledge about the environment and dynamics, a gated recurrent unit (GRU)-based DRL algorithm is formulated by considering the designed history state-action and estimated flow rates. In addition, the randomization technique is applied during training to distill the general control policy for the physical SMMR. The results of numerical simulations and experiments are illustrated to demonstrate the robustness and efficacy of the presented control framework. Finally, in-depth analyses and discussions indicate the potentiality of DRL for soft magnetic robots in biomedical applications.

A dynamic self-assembly is a promising approach for inducing the collective behavior of agents to perform coordinated tasks at small scales. However, efficient pattern formation and navigation in environments with complex conditions remain a challenge. In this article, we propose a strategy for micromanipulation using dynamically self-assembled magnetic droplets with needle guidance. An iron needle was controlled by a three-degree-of-freedom (3-DoF) manipulator and magnetized by precessing magnetic fields. The process of self-assembly was optimized based on real-time vision feedback and a genetic algorithm. Affected by the locally induced field gradient near the needle, reconfigurable assembled magnetic droplets were formed beneath the air-liquid interface with high time efficiency, and the geometric center of the pattern was determined. Following the magnetized needle, assembled patterns were navigated along preplanned paths and exhibited reversible pattern expansion and shrinkage. Moreover, cargo can be trapped and caged by exploiting the induced fluid flow around the assembled droplets. To perform cargo transportation tasks in a multiple-obstacle environment, an optimal path planner with obstacle-avoidance capability was designed based on the particle swarm optimization (PSO) algorithm. Experiments demonstrated effective pattern formation, navigation, cargo trapping, and obstacle-avoidance transportation. The proposed method opens new prospects of using a dynamically self-assembled pattern as an untethered end-effector for micromanipulation.Note to Practitioners—This article was motivated by the recent interest in utilizing the collective behavior of small-scale active agents to perform micromanipulation tasks. Driven by external magnetic fields, building blocks are gathered and assembled, yielding a dynamically stable pattern. To perform practical tasks, efficient pattern formation, control, and navigation are required. Besides, obstacles often exist in the working environment, challenging pattern navigation, and manipulation tasks. The strategy presented here is developed for micromanipulation using dynamically self-assembled magnetic droplets with needle guidance. The three-axis Helmholtz coil system is applied to rotate the droplets and magnetize the iron needle. Algorithms are designed to guide and optimize the pattern formation, navigation, and cargo trapping process. Magnetic droplets are real-time tracked, and ordered assembled patterns are formed in an optimized way. Following the needle, the pattern was navigated and performed cargo manipulation tasks with obstacle-avoidance capability. Experimental results have validated the proposed strategy in pattern formation, navigation, and cargo manipulation in a multiple-obstacle environment.

After years of development, various swarms of robots have been proposed for many complicated tasks, such as forming patterns, cooperative locomotion, and adapting to different environments. However, controlling microrobotic swarms is still a challenging task owing to the lacking of integrated devices on the small-scale agents, and actuation of multiple microrobotic swarms to different targets under the same global input will be even more difficult. In this work, we present a swarm of nickel nanorods and its diverse locomotion velocity compared with Fe3O4nanoparticle swarms is implemented for actuating the two swarms to different targets under the same customized oscillating magnetic field. The effects of the magnetic anisotropy of agents on the macroscopic swarm behaviour are analysed theoretically. To prove the strategy, the speeds of the two swarms were characterized through experiments, and demonstrations were conducted to show the capability of driving the two swarms to different locations in the same environment. Furthermore, parallel locomotion of the two swarms towards opposite directions was also achieved on a tilted substrate. This work has proved the feasibility of simultaneously actuating two swarms to diverse targets and promoted fundamental understandings of microrobotic swarms.

We propose ultrasound Doppler imaging and magnetic navigation of collective cell microrobots in whole blood. Cell microrobots are cultured using stem cells and iron microparticles, they have spheroidal structures and can be actuated under external magnetic fields. A collective of cell microrobots can be reversibly gathered and spread due to the tunable magnetic interaction, and are able to exhibit collective motion in whole blood under rotating magnetic fields. Simulation results indicate that the induced blood flow around the collective pattern affects the motion of red blood cells (RBCs), and experimental results show that Doppler signals are observed when emitting ultrasound waves to the microrobots. The induced Doppler signals are affected by the input field frequency and the ultrasound parameters (pulse repetition frequency). Due to the induced three-dimensional blood flow, Doppler signals can be observed when the imaging plane is above the collective microrobots, which enables indirect localization when performing navigation on an uneven surface. Our study investigates a strategy for pattern formation and navigation of collective microrobots under ultrasound Doppler imaging, demonstrating that the integration of collective control approach and medical imaging holds great potential for real-time active delivery tasks.

We propose thrombolysis using a magnetic nanoparticle microswarm with tissue plasminogen activator (tPA) under ultrasound imaging. The microswarm is generated in blood using an oscillating magnetic field and can be navigated with locomotion along both the long and short axis. By modulating the input field, the aspect ratio of the microswarm can be reversibly tuned, showing the ability to adapt to different confined environments. Simulation results indicate that both in-plane and out-of-plane fluid convection are induced around the microswarm, which can be further enhanced by tuning the aspect ratio of the microswarm. Under ultrasound imaging, the microswarm is navigated in a microchannel towards a blood clot and deformed to obtain optimal lysis. Experimental results show that the lysis rate reaches -0.1725 ± 0.0612 mm3/min in the 37°C blood environment under the influence of the microswarm-induced fluid convection and tPA. The lysis rate is enhanced 2.5-fold compared to that without the microswarm (-0.0681 ± 0.0263 mm3/min). Our method provides a new strategy to increase the efficiency of thrombolysis by applying microswarm-induced fluid convection, indicating that swarming micro/nanorobots have the potential to act as effective tools towards targeted therapy.

Microrobotic delivery possesses a 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 article, we propose an automated control approach for a new type of magnetic microrobot, i.e., the multifunctional magnetic spore (Mag-Spore), which has good potential for targeted delivery. By the surface functionalization of the spore with Fe3O4nanoparticles and carbon quantum dots (QDs), it can be remotely actuated and tracked by an electromagnetic coil system and the fluorescence microscopy, respectively. Our control approach uses fluorescence imaging for vision feedback, which enhances the recognition and tracking of Mag-Spores, obstacles, and cells. Then, information of the obstacles, targeted cells, and Mag-Spores for planning and control is identified by 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, a robust model predictive trajectory-tracking controller is synthesized. Simulations are conducted to validate the proposed control approach and tune the control parameters. Experiments demonstrate the effective targeted delivery of the Mag-Spore by using the proposed automated control method under the guidance of fluorescence imaging. Note to Practitioners-This article was motivated by the recent wide interest of precise targeted delivery using biohybrid magnetic microrobots. Driven by external magnetic fields, microrobots accomplish the targeted delivery tasks. In practical applications, obstacles and obstructed optical feedback often exist that make the delivery task challenging. The Mag-Spore presented here has a hollow structure, so that the cargo-carrying capability is maximized and supported by the proposed automated control techniques, and the delivery precision and efficiency are promised in multiple-obstacle scenarios. In addition, the control method has the robustness to model uncertainties and external disturbances that should be considered and well solved in applications. Fluorescence imaging, a common way for observing biomaterials, is compatible with the proposed control scheme and the developed software so that the recognition and tracking of the Mag-Spore and other biomaterials are improved. Moreover, the self-established plug-and-play (PnP) electromagnetic magnetic coil system has the feature of easy installation and configuration on fluorescence microscopes. Simulations and experiments validate the effectiveness of our method in fluorescence-guided targeted delivery using magnetic microrobots.

Dynamic self-assembly is treated as a promising approach for generating a robotic swarm to perform coordinated tasks, and the assembled pattern can be tuned by regulating the energy input. However, location of a dynamically assembled pattern is hard to be determined, especially under global fields, such as magnetic field. In this paper, we report the formation and manipulation of dynamic self-assembled droplets at the air-liquid interface with the assistance of a magnetic needle. Affected by the locally induced field gradient near the needle, reconfigurable assembled droplets are obtained with higher time-efficiency, and the location of the pattern can be determined. The pattern is reversibly tuned to exhibit expansion and shrinkage by adjusting the height of the needle. Assembled droplets are able to be steered via following the needle in a controlled manner. Moreover, cargo is trapped by exploiting the induced rotational flow around the droplets, and it can also be caged into the central area of the pattern and transported to the desired location. The proposed method opens new prospects of using energy-dissipative pattern as an untethered end-effector for microrobotic manipulation.

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.