Microrobot swarms with locomotion dexterity and shape reconfigurability show immense potential in biomedical applications. Automatic control strategies are critical for the navigation of swarms in unstructured environments. Existing control methods mainly focus on initial and final states of swarms, and swarm process control designed to avoid undesired states throughout the control process is yet investigated. In this work, we develop a deep learning-based process control strategy for swarms guided by phase diagrams. Two deep neural networks are respectively built to model the swarm shape and kinematics. Control approaches based on precise swarm models are designed to automatically tune multiple swarm parameters. A phase diagram-based controller is proposed to guide the swarm reconfiguration while eliminating the coupling effects between swarm parameters. The swarm is enabled to accurately track predefined trajectories while performing continuous reconfiguration with desired states during the entire process. By integrating the process control of swarm pattern and locomotion, the swarm can dynamically adapt to constrained unstructured spaces and achieve robust collision avoidance.

Reconfigurable microswarms have received extensive attention recently. In this work, we propose a control strategy for a ribbon-like swarm to perform navigated locomotion with a stable pattern, and perform controllable splitting into double subswarms to reach two targets simultaneously. Two different behaviors of the ribbon-like swarm are firstly investigated, i.e., locomotion with a stable pattern, and controllable splitting. The two behaviors of the ribbon-like swarm are realized based on different aspect ratio of the swarm. Subsequently, we propose a morphology controller to keep the aspect ratio of the swarm within a desired range. The morphology controller consists of a feedforward controller and a PD controller. The feedforward controller containing a fitted model, and a fuzzy logic controller for online compensation of the model error. The control strategy combining the morphology planning, morphology controller, path planning, and motion controller is developed. Using the proposed control strategy, the ribbon-like swarm can be navigated to follow a desired path with a stable pattern while avoiding obstacles, and finally perform controllable splitting into double subswarms to reach two predefined targets simultaneously.

Magnetic field-driven microrobotic swarms have drawn extensive attention, especially in the field of automatic control. Realizing dynamic path planning and motion control of microrobotic swarms for mobile target tracking is one of the important tasks that still remains unsolved. In this paper, we firstly present an enhanced bidirectional rapidly-exploring random tree star (EB-RRT*) algorithm considering the physical size of the swarm to dynamically plan the optimal path for obstacle avoidance. An image-guided motion controller, which consists of a direction controller and a Genetic Algorithm based Linear Quadratic Regulator (GA-LQR) velocity controller, is then proposed to realize mobile target tracking using microrobotic swarms. Targeted bursting algorithm is subsequently developed to meet the requirement of tracking high-speed (i.e., 20$\mu m/s$) mobile targets. Simulations are performed to validate the proposed methods and obtain the proper ranges of the input parameters for the controllers. Finally, the control effectiveness of mobile target tracking in different conditions and environments is validated by experimental results. Note to Practitioners—The motivation of this work is to develop an effective control scheme for mobile target tracking using microrobotic swarms. Conventional control schemes mainly focus on the control of single microrobots to reach static targets, and thus the desired path is fixed once planned. In addition, the motion of single monolithic microrobots can be modelled precisely. However, in mobile target tracking using microrobotic swarms, dynamic planning algorithms are demanded to frequently update the desired path. Swarms consisting of millions of micro-agents are also difficult to be modelled due to the complex agent-agent interactions. In this work, an effective control scheme consisting of a dynamic path planner, a motion controller and a targeted bursting unit is developed. Real-time dynamic paths will be planned even though the positions of the swarm and the target change rapidly. The precise control of the swarm direction and velocity are achieved, and moreover, using the targeted bursting algorithm, the swarm can be accelerated to approach mobile targets accurately with higher efficiency. Experimental results validates the proposed tracking strategy in different environments with virtual obstacles. The proposed control scheme paves the way for a better understanding of advanced motion control methods for microrobotic swarms.

Magnetic miniature robots are promising tools for minimally invasive and noninvasive therapy. Constructing systems with actuation–perception loops is an essential step to progress from fundamental research to clinical applications, and from manual to automated manipulation. Such systems include imaging devices for tracking miniature robots inside a living body, and magnetic actuators for manipulating the robots. In this survey article, the designs, features, and control of various magnetic actuation systems with imaging modalities are summarized. The strategies of actuation–perception cooperation are discussed from both hardware and software aspects, aiming to provide a paradigm for building automated image-guided systems in clinical scenarios. Furthermore, the solutions when both the systems and surgeons simultaneously participate in the operation are introduced. We also discuss the advantages and drawbacks of reported techniques, major challenges, and potential prospects in this field.

Control and navigation of microrobotic swarms have drawn extensive attention recently. Avoiding dynamic obstacles using swarms is one of the major challenges that still remain unsolved. In this work, we develop a control strategy to navigate microrobotic swarms to targeted positions while avoiding dynamic obstacles. We first propose a criterion to evaluate the real-time locomotion efficiency during dynamic obstacle avoidance, i.e., the swarm moving direction and the distance between the swarm and the target. Subsequently, a hierarchical radar with three functional boundaries (detection, safety, and prediction circle) is designed for swarms. The optimal moving direction of the swarm with the existence of dynamic obstacles is selected based on the three circles. The effectiveness of the algorithm is validated by simulations and experiments. Using the proposed strategy, the swarm is capable of avoiding multiple moving obstacles and reaching the predefined target. Finally, to show the compatibility of the proposed control method, the swarm is deployed in a micromaze with different dynamic obstacles, and the results also validate the effectiveness of the strategy.

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.

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.