Caterpillar-type robots are widely used for medium- and large-sized pipe inspections. However, existing prototypes smaller than 80 mm lack both an active variable diameter capability and a contact force sensing function, which are crucial for safe and automatic exploration of unknown rigid/soft pipes (e.g., the colon). This study develops a variable diameter caterpillar-type miniature robot (VCMR) featuring a small size of Φ34.6 mm × 41 mm, a large variable-diameter range of 34.6-89.6 mm, and an integrated contact force sensing function. The VCMR actively adapts to pipe diameter changes using contact force feedback, demonstrates high load capacity in both vertical and horizontal rigid/soft pipes, and traverses a 150-cm colon phantom with sharp bends at an average velocity of 3.07 ± 0.48 cm/s. It holds promise for exploring tiny variable-diameter rigid/soft pipes and delivering cargoes through such pipes.

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.

Magnetically actuated robots have recently shown great capabilities for remote applications in medical procedures. However, the efficient actuation of magnetic robots with dexterous field and gradient generation in large workspaces remains challenging. To overcome the critical challenges, we report a reconfigurable electromagnetic actuation system (REMA) for regulating magnetic fields (maximum: 17 mT) and gradients (maximum: 120 mT/m) in large workspaces. Reconfigurable coil configurations are achieved by employing three mobile electromagnetic coils mounted on three independent 6-DOF robotic arms. Furthermore, the field characteristics generated by a single coil and three coils were modeled via Finite-element method (FEM) and measurements from experiments, respectively. Since there are non-linearities between desired field generation and coil configuration, we propose a multi-objective optimization (MOO) method for generating the Pareto-optimized coil configuration to achieve field and force control in large workspaces. Finally, extensive experiments were conducted to demonstrate the capability and dexterity of our system for autonomous magnetic manipulation in large workspaces, thus showing its potential for clinical applications. Note to Practitioners—This paper aims to address the dexterous generation of magnetic fields and gradients in large workspaces, aiming to realize accurate, efficient, and automated control of different magnetic robots. This paper introduces a reconfigurable electromagnetic actuation system based on three independent robotic arms with three electromagnetic coils. Subsequently, we propose a multi-objective optimization (MOO) method to regulate the coil configuration for generating different fields and gradients. This approach facilitates the application of magnetically driven helical robots, catheters, and capsule robots in various medical scenarios. The results demonstrate that our proposed platform and optimization strategy can effectively implement magnetic manipulations across diverse application scenarios. Looking ahead, we anticipate integrating our work with medical imaging devices to furnish doctors with enhanced tools for medical applications.

Self-balancing walking exoskeletons (SBWEs), which enable paralyzed individuals to walk without assistive devices, have been increasingly employed in rehabilitation training. This paper proposes a Kelvin-Voigt viscoelastic model-based bioinspired viscoelastic compliance controller (BVCC) for a novel SBWE named AutoLEE-II, which features high structural rigidity, low leg inertia relative to center of mass (CoM), and small hip joint axis misalignment between the user and SBWE. First, a novel series-parallel hybrid mechanism is designed for AutoLEE-II. This mechanism, inspired by the lower limbs of humans, reduces hip axis misalignment between the user and SBWE, decreases leg inertia relative to CoM, and improves structural stiffness. Second, a BVCC mimicking biological muscle is proposed to introduce viscoelastic compliance to SBWE to maintain locomotion stability of the SBWE during standing and walking. The BVCC is robust to the variable physical parameters of different users. Finally, self-balancing walking experiments are conducted with AutoLEE-II with empty load, manikin load and human subject load to validate the performance of AutoLEE-II and the proposed compliance controller BVCC. Note to Practitioners—This paper aims to design a self-balancing walking exoskeleton (SBWE) that provides rehabilitation training exercise and walking assistance services for individuals with hemiplegia, paraplegia, and quadriplegia. First, we biomimetically designed the mechanical structure of the SBWE, named AutoLEE-II based on the distribution of human joints and connecting links. The bionic mechanism reduces axis misalignment between the SBWE and users, improves stiffness and reduces the inertia of the legs relative to center of mass. We then designed a bioinspired viscoelastic compliance controller (BVCC) based on the centroid dynamics model, which is robust to the physical properties of the user and introduces the SBWE with active compliance. Finally, self-balancing walking experiments with an empty load, a manikin load and human subject loads are performed to validate mechanical structure of the proposed AutoLEE-II and the locomotion stability of the physical parameter robust BVCC.

Magnetic continuum robots (MCRs) have become popular owing to their inherent advantages of easy miniaturization without requiring complicated transmission structures. The evolution of MCRs, from initial designs with one embedded magnet to current designs with specific magnetization profile configurations (MPCs), has significantly enhanced their dexterity. While much progress has been achieved, the quantitative index-based evaluation of deformability for different MPCs, which can assist in designing MPCs with enhanced robot deformability, has not been addressed before. Here we use “deformability” to describe the capability for body deflection when an MCR forms different global shapes under an external magnetic field. Therefore, in this paper, we propose methodologies to design and control an MCR composed of modular axially magnetized segments. To guide robot MPC design, for the first time, we introduce a quantitative index-based evaluation strategy to analyze and optimize robot deformability. Additionally, a control framework with neural network-based controllers is developed to endow the robot with two control modes: the robot tip position and orientation ($M_{1}$) and the global shape ($M_{2}$). The excellent performance of the learnt controllers in terms of computation time and accuracy was validated via both simulation and experimental platforms. In the experimental results, the best closed-loop control performance metrics, indicated as the mean absolute errors, were 0.254 mm and 0.626$^\circ$for mode$M_{1}$and 1.564 mm and 0.086$^\circ$for mode$M_{2}$.

Magnetic continuum robots (MCR) have shown great potential in minimally invasive interventions because they can be actively and remotely navigated through complex in vivo environments. However, the deformation capability of current MCRs is limited by fixed magnetization congurations, preventing them from accessing hard-to-reach areas. This is due to the fact that under a global magnetic field, fixed magnetization conguration causes the magnets on the MCRs exposed to coupled magnetic forces and torques, resulting in a lack of controllable degrees of freedom. Here, we introduce a reprogrammable magnetic continuum robot (RMCR) enabled by magnetic reprogramming modules (MRM). Actuated by shape memory alloys, the magnetic moment direction of MRMs can be selectively reprogrammed in real-time and in-situ. Magnetic reprogramming capabilities enable the RMCR to achieve complex shape transformations. Results show that the range of motion in the tip direction of the RMCR increases by 193% compared with regular MCR. Besides, MRMs on the RMCR can achieve active attraction and separation under simple magnetic fields. The reprogramming process of the RMCR is theoretically investigated. A design methodology for MRMs is then proposed and the fabrication process of RMCR is described in detail. Furthermore, a kinematic model of the RMCR is established, simulated, and experimentally validated.

In this work, we study the optimal parameter design and microrobotic navigation control of the parallel-mobile-coil system (PMCS) that consists of three mobile electromagnetic coils. With motion driven by a parallel mechanism, the three coils can move in 3D large space and keep as close as possible to the controlled microrobot for magnetic actuation. Although promising for microrobotic applications, how to design such a type of system for a specific workspace requirement is untackled. Regarding this issue, we propose a computational design method, by which one can calculate the structural parameters of a PMCS starting from a required cylindrical workspace. With the derived performance metrics for motion actuation and magnetic actuation of the PMCS, the system actuation performance (composed of motion and magnetic actuation) is optimized. Utilizing the design method, we optimally construct a prototype PMCS for microrobotic navigation. We then conduct experiments to validate the demanding field/force generation capability of the PMCS and demonstrate the navigation control of different types of magnetic microrobots. In particular, we design closed-loop motion controllers for both torque and force-driven microrobots, using which automated large-workspace and high-accuracy trajectory tracking is realized. Note to Practitioners—This work is motivated by the recent wide interest in magnetic microrobots. Driven by external magnetic fields, magnetic microrobots can navigate in a wireless manner for targeted delivery/therapy. To promote microrobot applications to the human body, a magnetic actuation system with large workspace is desirable. However, due to the fast decay of magnetic field, the commonly used stationary coil-based magnetic actuation systems have the workspace scalability problem. Thus, several mobile-coil-based systems have been designed. In this work, we propose an optimal design method for the PMCS, using which one can design a PMCS starting from a cylindrical workspace with performance being optimized. We construct a PMCS prototype with a workspace of$\Phi 230 \times 100$mm3, and we then study the automated microrobotic navigation control methods for the PMCS. Controllers are designed for different types of magnetic microrobots, and experiments show that, using the controllers, the PMCS can perform automated large-workspace microrobotic navigation control with high accuracy.

Precise trajectory control is imperative to ensure the safety and efficacy of in vivo therapy employing the magnetic helical millirobots. However, achieving accurate 3-D path following of helical millirobots under fluid flow conditions remains challenging due to the presence of the lumped disturbances, encompassing complex fluid dynamics and input frequency saturation. This study proposes a robust 3-D path following control framework that combines a disturbance observer for perturbation estimation with an adaptive finite-time sliding mode controller for autonomous navigation along the reference trajectories. First, a magnetic helical millirobot's kinematic model based on the 3-D hand position approach is established. Subsequently, a robust smooth differentiator is implemented as an observer to estimate disturbances within a finite time. We then investigate an adaptive finite-time sliding mode controller incorporating an auxiliary system to mitigate the estimated disturbance and achieve precise 3-D path tracking while respecting the input constraints. The adaptive mechanism of this controller ensures fast convergence of the system while alleviating the chattering effects. Finally, we provide a rigorous theoretical analysis of the finite-time stability of the closed-loop system based on the Lyapunov functions. Utilizing a robotically-actuated magnetic manipulation system, experimental results demonstrate the efficacy of the proposed approach in terms of the control accuracy and convergence time.

Animals, including humans that have muscles with viscoelastic compliance can achieve improved stability. Hence, we investigate a biomimetic control framework with viscoelastic compliance and subsequently apply it to a self-balancing lower limb exoskeleton robot (SBLLER), to ensure stability during locomotion. This article presents a novel biomimetic viscoelastic compliance control framework (VCCF) for an SBLLER that enables wearers to walk without the need for crutches or other external stabilization tools during self-balancing locomotion and rehabilitation training tasks. First, we devised a biomimetic viscoelastic mathematical model (BVMM) and subsequently analyzed its viscoelastic properties. Second, the VCCF, which incorporates the desired and real center of mass (CoM) and a BVMM that connects the desired and real CoMs, is designed to introduce active viscoelastic compliance for an SBLLER so that the SBLLER can absorb the early landing impact forces and stabilize itself. In addition, the VCCF exhibits robustness to the physical parameters of diverse wearers and can effectively accommodate a wide range of subjects, which is essential for promoting the application of exoskeletons. Finally, the validity of the proposed VCCF is confirmed through disturbance and walking experiments.

Remote-actuated magnetic robots, relying solely on the magnetic torque stemming from rotating magnetic fields, hold immense promise in biomedical applications. However, to precisely actuate magnetic robots in large workspaces, the efficient generation of isotropic rotating fields using electromagnetic actuation (EMA) systems presents an enduring challenge. This is because the choice of configuration of the EMA system is a major concern, particularly when considering collision avoidance between coils and the human body while ensuring isotropic actuation. In this study, we presented an analysis of the characteristics of various three-coil configurations by quantitatively evaluating field isotropy. Furthermore, we introduced a performance-guided optimization method to adjust coil configurations by optimizing designed evaluation metrics, aiming to generate rotating fields with isotropic characteristics in a target local region. Finally, we implemented a reconfigurable EMA and conducted extensive experiments to demonstrate the capability of our method and platform. The experimental results showcase the potential of our approach for advanced clinical applications.

Untethered microrobots possess a promising perspective for micromanipulation applications. With specifically designed morphologies and structures, microrobots are able to perform controllable delivery of target objects. However, the manipulation process still lacks autonomy, to achieve which the mechanism of picking, transporting, and releasing behaviors needs further investigation. In this article, we propose to achieve automated microrobotic manipulation using magnetic microswarms with multimodal morphology. The microswarm is composed of around 11–21 million$\text{Fe}_{3}\mathrm{O}_{4}$nanoparticles (1.0$\text{--}1.8\,\mu$L particle suspension). When exposed to different dynamic magnetic fields, the swarm could exhibit corresponding forms. We realize precise and controllable cargo picking and releasing by exploiting the fluid fields of different swarm forms. In order to quantitatively describe these behaviors, we design a finite-state machine. A super-twisting sliding-mode controller has been formulated for the motion control of swarms. The disturbances are compensated via a disturbance observer. To enable automated micromanipulation in obstructed scenarios, a path planner inspired by rapidly exploring random tree algorithm is designed for path planning when obstacles exist. We also propose an enhanced-genetic algorithm to optimally transport multiple objects to the target position. Experiments demonstrate that our method could effectively transport micro-objects with different sizes and shapes. The precise selectivity of the method is validated when multiple objects exist in the working environment. Finally, the long-distance delivery ability and adaptivity to various friction situations of our strategy are demonstrated. This work explores a concise, untethered, and automated micromanipulation strategy, provides a new automatic tool for micromanipulation tasks, and extends the application potential of swarm microrobotics.

Accurately estimating uncertain body disturbances is critical for the effective integration of wearable exoskeletons for active human users. In this article, considering nonlinear time-varying human disturbances, we propose a TDE-BVC feedback control method that performs biomimetic viscoelastic compliance (BVC) with transformer-based deformation estimator (TDE). The method provides human-exoskeleton with stable and dynamic walking capabilities. We developed a transformer-based end-to-end deformation estimation sequence network that simultaneously captures the mapping relationship between foot force/torque and exoskeleton deformation. Moreover, we integrated the BVC to eliminate the impact and external disturbances experienced by the human-exoskeleton, enabling it to closely follow a reference gait, and utilized Lyapunov’s theorem to prove its stability. The control strategy is independent of the parameters of the human exoskeleton. To evaluate the effectiveness of the proposed method, walking experiments were conducted on different subjects. Our results indicate that with only 6-axis force/torque sensors, the TDE-BVC controller could accurately estimate and compensate for the deformation of different human-exoskeletons in each control cycle$(p\lt 0.001)$, with robustly stable and adaptive dynamic walking within a bounded control error.

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.

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.

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.

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.

The use of magnetic fields and field gradients to move magnetic material and devices within the human body has a surprisingly long history. Over the past two decades, there has been renewed interest in this area with the growth of magnetic medical microrobots. In this article, we focus on the state-of-the-art and future directions for magnetically actuated medical robots from anin vivoperspective. We initially review the history and relevant physics followed by a discussion on the limitedin vivoresearch efforts that investigate magnetically guided devices. Our focus is on magnetically guided tethered probes, untethered devices (microrobots and nanorobots), and magnetic navigation systems that have been or could be utilizedin vivoto provide increased control and safety for the physician and patient.

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.

The small size and wireless actuation of microrobots make them potential candidates for minimally invasive medicine. To advance microrobots to future clinical application, microrobotics researchers have investigated a number of key issues, in which swarm control is a primary challenge and is attracting increasing attention. As a single microrobot has limited volume and surface area, clinically relevant tasks, includingin-vivotracking, usually require simultaneous control of a large swarm of microrobots. Unlike macroscale robots, implementing on-board actuators and sensors for microrobots is challenging, which differentiates swarm microrobotics from other swarm robotics approaches. This article systematically summarizes the state of the art for this emerging field, including actuation systems with different power sources, swarm behaviors modeling and simulation, swarm control strategies, and targeted biomedical applications. Actuation principles of microrobot swarms are categorized in detail, and critical comparisons are made to provide guidance and insight for future swarm microrobotics researchers. Considering the unique features of swarm microrobotics compared to traditional swarm robotics, this article also emphasizes the modeling, simulation, and control of microrobot swarms. Furthermore, recent biomedical applications of microrobot swarms are summarized to illustrate specific application scenarios. Finally, we provide an assessment of the future directions of swarm microrobotics.

Reconfigurable microrobotic swarms and controllable active matter systems have drawn extensive attention recently. Developing effective actuation strategies and control schemes that enable embodied intelligence of microscopic swarms are both major challenges. In this work, we realize the generation of an elliptical paramagnetic nanoparticle swarm (EPNS) with enhanced dexterity for adaptive locomotion, and subsequently a fuzzy control strategy is developed for automatically tuning pattern deformation, orientation, and position of the swarm. By adjusting the input field, the aspect ratio of the EPNS will change accordingly, and we demonstrate its adaptive navigation through curved and narrowed channel by performing pattern reconfigurations. Moreover, using the proposed control strategy, precise matches can be reached between the controlled swarms and the desired patterns. Finally, to show the high compatibility of the control strategy, we employ ribbon-like colloidal swarms driven by oscillating magnetic field, and the results also validate the effectiveness of the strategy.

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.

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.

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.

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.

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.

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.

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.

Swarm control is one of the primary challenges in microrobotics. For the automated control of such a microrobotic system with small size and large population, conventional methods using precise robot models and robot-robot communications lose effectiveness due to the complex locomotion of micro/nano agents in a swarm and difficult implementation of onboard actuators and sensors for individual motion control and motion feedback. This article proposes a statistics-based approach and reports the fully automated control of a swarm of paramagnetic nanoparticles including the swarm pattern formation, identification, tracking, motion control, and real-time distribution monitoring/control. By establishing the swarm statistics, collective behaviors of a nanoparticle swarm can be quantitatively analyzed by computers. Algorithms are designed based on the statistics to automatically generate and identify the vortex-like paramagnetic nanoparticle swarm (VPNS), which present robustness to the dose and initial distribution of the nanoparticle swarm. In order to robustly track a VPNS, a statistics-based tracking method is proposed, in which 500 boundary points of the VPNS are extracted and the VPNS distribution is optimally recognized. And, with the proposed gathering improvement control, experiments show that over 70% nanoparticles can be gathered in the VPNS. Furthermore, an automated motion control scheme for the VPNS is proposed which shows high-accuracy trajectory tracking performance (tracking error: <; 5% body length). Besides, real-time monitoring of the distribution region/density and control of the distribution area for a nanoparticle swarm are also realized by using the statistics. Experimental results validate the feasibility of the proposed method in automated control of paramagnetic nanoparticle swarms.

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.

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.

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 Fe3O4 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.

Microrobots are considered as potential candidates for targeted delivery of cargos, drugs and even energy with high precision. One interesting phenomenon is their collective behaviour actuated by dynamic fields, which is yet to be adequately studied. Herein, we report a novel method of using millions of magnetic nanoparticles to generate a dynamic-equilibrium particle-based vortex, which can manipulate multiple cargos simultaneously at the microscale. The governing physical laws of the generation of a particle-based vortex are explained and the experimental results are presented. The high effectiveness of this micro-vortex-based method of particle gathering is testified. Moreover, the vortex can be navigated near a solid surface in a controlled manner. The velocity and morphology of the mobile vortices with different pitch angles are investigated, showing that the vortex moving with small pitch angles is capable of maintaining the original shape and coverage area. Collecting and transporting multiple polystyrene (PS) microbeads into a channel using the vortex are also demonstrated. This method allows us to perform micromanipulation using the collective behaviour of nanoparticles and to develop new strategies for the formation and control of the microrobotic swarm.

Paramagnetic nanoparticles are considered as attractive building blocks, particularly for robotic delivery of drugs. Although paramagnetic nanoparticles can be effectively gathered and transported using external magnetic fields, the disassembly process is yet to be fully investigated to avoid the formation of aggregations. In this paper, we report a novel method of controllable disassembly of paramagnetic nanoparticle chains using a predefined dynamic magnetic field. The dynamic field is capable of performing spreading and fragmentation of the particle chains simultaneously. Using the magnetic dipole-dipole repulsive forces, the final area covered by the particle chains swells up to 545% of the initial area. The final length distribution presents a strong relationship with the frequency of the dynamic field in deionized (DI) water and two kinds of biofluids. An analytical model of phase lag is proposed, which shows good agreement with the experimental results. Furthermore, we also present an assembly process using a rotating magnetic field, indicating that the assembly disassembly process is reversible. In addition, batch-cargo delivery of polystyrene microbeads using the nanoparticle chains as swarm-like nanorobots is demonstrated.

We present a general solution for steering microrobotic swarm by dynamic actuating fields. In our approach, the motion of micro-robots is controlled by changing the actuating direction of a field applied to them. The time-series sequence of actuating field's directions can be computed automatically. Given a target position in the domain of swarm, a governing field is first constructed to provide optimal moving directions at every points. Following these directions, a robot can be driven to the target efficiently. However, when working with a crowd of micro-robots, the optimal moving directions on different agents can contradict with each other. To overcome this difficulty, we develop a novel steering algorithm to compute a statistically optimal actuating direction at each time frame. Following a sequence of these actuating directions, a crowd of micro-robots can be transported to the target region effectively. Our steering strategy of swarm has been verified on a platform that generates magnetic fields with unique actuating directions. Experimental tests taken on aggregated magnetic micro-particles are quite encouraging.

Helical microswimmers capable of propulsion at low Reynolds numbers have been proposed for numerous applications. Several different kinds of helical swimmers inspired by E. coli bacteria have been proposed by researchers, and most of them have rigid helical tails. However, high softness could make swimmers more adaptive in confined environments for biomedical applications. This paper aims to study the morphologies and the swimming characteristics of magnetically actuated swimmers with belt-like soft tails initially straight at low Reynolds numbers. We found that a swimmer with a soft tail during rotations shows different morphologies: a twisted shape until the input frequency increases to a threshold frequency, and a helical shape until a step-out frequency. Beyond the stepout frequency, the shape of the soft tail becomes irregular. The soft tail swimmers with different lengths show similar swimming velocities at same rotational frequencies. However, their maximal swimming velocities are different because of the varied step-out frequencies. The interactions between the soft tails reduce the swimming velocity, and this influence increases with the rotational frequency. Thus, the swimming performance is not improved by doubling the number of soft tails in our experiments.

Swimming microrobots have the potential to be used in medical applications such as targeted drug delivery. The challenges for navigating microrobots in the human body lie not only in the viscosity of body fluids but also in the existence of different types of fibers and cells such as blood cells or protein strands. This paper investigates artificial bacterial flagella (ABFs), which are helical microrobots actuated by an external magnetic field, in methyl cellulose solutions of different concentrations. It can be shown that the microrobots can be propelled in these gel-like heterogeneous solutions and successful swimming was demonstrated in solutions with a viscosity of more than 20 times that of water. Furthermore, results indicate that the existence of fibers can help ABFs swim more effectively, which agrees with previous experimental results reported for natural bacteria.

The artificial bacterial flagellum (ABF), a helical swimming microrobot, has the potential to be used for biomedical applications such as cellular and intracellular manipulation. The velocity and the propulsive force of the ABF can be controlled by the input frequency of the rotating magnetic field. In this paper the swimming behavior of the ABF near a solid surface is reported. Three regions have been observed for the frequency-dependent swimming behavior of the ABF, i.e. the step-out, the linear and the drift-dominated region. At low frequencies it has been found that the desired screw-type motion is replaced by a wobbling swimming movement with a frequency-dependent precession angle. Moreover, the experimental results show that the wobbling motion of the ABF enhances the undesired sidewise drift due to wall effects. Additionally, the cause of the precession motion has been investigated by a hydrodynamic model. Our results imply that the linear range of the input magnetic frequency and the output ABF velocity is not only limited by the applicable torque at high frequencies but also by the wobbling of helical swimming at low frequencies.

Modes of melting and evaporation of metal at attogram level from individual carbon nanotubes (CNTs) are investigated experimentally using nanorobotic manipulation inside a transmission electron microscope. We compared the melting and evaporation induced by electric current, Joule heating, charge, and ionization. Experiments show that the most effective method is by positively ionizing the encapsulated metal, therefore, an electrostatic field can be used to guide the flow. Applications and potential applications of mass transport and deposition in nanofluidic systems have been presented including self-welding, actuation, and storage.

Artificial bacterial flagella (ABF) are swimming microrobots that mimic the swimming motion of bacteria. The helical swimmer consists of an InGaAs/GaAs/Cr helical nanobelt tail fabricated by a self-scrolling technique with dimensions similar to a natural flagellum, and a thin soft-magnetic metal ¿head¿ consisting of a Cr/Ni/Au multi-layer. The swimming locomotion of ABF is precisely controlled in 3-D by external rotating magnetic fields. Microsphere manipulation is performed by ABF, and experimental results show that both the position and the orientation of microspheres can be precisely controlled. The propelling force of ABF is in the pico-Newton range. We also describe a swarm-like behavior in which three ABF swim in a pack, indicating the potential to handle several micro objects in parallel. Self-propelled devices such as these are candidates for wireless 6-DOF micro and nanomanipulation tools for handling cellular and sub-cellular objects.

Few rotational actuators currently exist with the ability to transmit motion at different speeds, torques, and directions at the nanometer scale. We present work regarding the application of helical nanobelts as linear-to-rotary and rotary-to-linear motion converters. We discuss their ability to rectify device rotation to linear motion for untethered microrobotic applications as well as their application as rotary sample stages for nanoscale imaging.

Nanorobotic spot welding using single-crystalline-copper-filled carbon nanotubes (CNTs) is investigated experimentally inside a transmission electron microscope (TEM). Controlled melting and flowing of copper inside nanotube shells are realized by applying bias voltages between 1.5 V and 2.5 V. The average mass flow rate of the copper was found to be 120 ag/s according to TEM video imaging (measured visually at approximately 11.6 nm/s through the CNT). Successful soldering of a copper-filled CNT onto another CNT using a nanorobotic manipulator shows promise for nano spot welding, which can play a role similar to its macro counterpart for the interconnection of nano building blocks for the assembly of nanoelectronic circuits and nanoelectromechanical systems (NEMS).

Robotic manipulation at the nanometer scale is a promising technology for structuring, characterizing and assembling nano building blocks into nanoelectromechanical systems (NEMS). Combined with recently developed nanofabrication processes, a hybrid approach to building NEMS from SiGe/Si/Cr nanocoils and Si/Cr nanospirals is presented. Nanosensors and nanoactuators are investigated from experimental, theoretical, and design perspectives