Magnetic microrobotic swarms are increasingly studied for their potential in precise motion control under complex and uncertain environments. One of the core challenges in swarm-level control lies in achieving accurate trajectory tracking in the presence of time-varying dynamic parameters, such as fluid resistance. To address such problem, we incorporate error integration into the swarms control framework and derive a kinematic model through model transformation. Based on this model, a Lyapunov-based adaptive robust control strategy is developed to ensure closed-loop stability and compensate for environmental uncertainties. The proposed controller dynamically adapts to unknown environmental variations, and rigorous theoretical analysis establishes the asymptotic stability of the system. A series of experiments are conducted on an electromagnetic actuation platform using silicone oil environments with different viscosities (5 cSt, 10 cSt, and 15 cSt) as well as under a rapidly varying viscosity generated by syringe pump to validate the method. Experimental results confirm that the proposed strategy enables the microrobotic swarm to achieve stable and precise trajectory tracking under varying resistance conditions, demonstrating its robustness and adaptability.

Three-axis orthogonal six-coil electromagnetic actuation (EMA) systems are widely used for magnetic microrobot control due to their structural simplicity and large workspace. However, such systems suffer from rank deficiency in their actuation matrix, which often leads to singularities and inaccurate force generation when using conventional current allocation methods based on the Moore-Penrose pseudoinverse. This approach tends to unevenly distribute currents, resulting in premature saturation of certain coils and limiting the overall magnetic force output. To overcome these limitations, this paper introduces a novel actuation strategy that enhances both trajectory tracking accuracy and electromagnetic performance. The proposed method consists of two key components: first, an angle selection scheme based on the desired magnetic force, which ensures solvability and introduces redundancy in the current solution; second, an infinity-norm current optimization strategy that minimizes the maximum coil current, thereby increasing the achievable magnetic force under current constraints. Simulation analysis demonstrates that the proposed method increases the maximum magnetic force by 41.5% compared to the traditional approach. Experimental validations were conducted on planar trajectory tracking, including figure-eight and spiral paths with increasing velocity. These validations confirm significant improvements in both actuation accuracy and force capability.

In recent years, local magnetic field actuation technology has garnered significant attention in the field of collaborative control of multiple micro-robots. By optimizing the design of coil array structures, gradient magnetic fields can be generated in target areas, enabling independent control of multi-robot systems. However, existing research is largely limited to step-by-step actuation modes, which often cause noticeable jitter during robot movement. Moreover, they make it difficult to achieve omnidirectional and continuous motion, severely limiting both motion smoothness and positioning accuracy. To address these issues, this study proposes a coil array actuation platform and introduces a differential current actuation strategy, effectively achieving smooth motion control of multi-robot systems. The research first analyzes the spatial magnetic field distribution characteristics through modeling; then, based on the magnetic force model, a differential current actuation strategy for multiple robots is proposed; finally, an experimental platform is constructed and a series of experiments are conducted. The experimental results show that this actuation platform can achieve independent and smooth control of multiple micro-robots, demonstrating promising potential in applications such as automated microscopic manipulation.

The collaboration of microrobot teams has attracted considerable attention, particularly in the field of micro/nano manipulation. Achieving independent control and motion planning of multiple magnetic microrobots for coordinated movements is one of the most important tasks that is still unsolved. In this paper, a$12\times 12$coil array system is developed to generate a series of localized magnetic fields that enable simultaneous control of multiple identical magnetic microrobots, allowing teams of microrobots to collaborate in parallel for micromanipulation tasks. First, the structure of the microcoil is optimized based on the finite element model to increase the strength and gradient of the magnetic field, which in turn enhances the driving performance of the system. Meanwhile, an improved multi-target tracking algorithm that utilizes kernel correlation filtering (KCF) and image contour detection (ICD) techniques is proposed to improve the tracking accuracy of microrobots. In addition, collaborative planning for multiple magnetic microrobots is also achieved with the combination of the conflict-based search (CBS) algorithm. Finally, the developed system is tested with extensive physical experiments. Especially, experiments on magnetic droplet transport with two microrobots are also conducted. The results impressively demonstrated the effectiveness of the devised system and the proposed methods. Note to Practitioners—This article is motivated by the recent wide interest in magnetic microrobots. Actuated by external magnetic field, magnetic microrobots can wirelessly perform targeted delivery/therapy and other micro-assembly tasks. To facilitate collaboration between microrobots, independent control of each microrobot is desirable. However, due to the interaction between magnetic microrobots and the global magnetic field, the collaboration of multiple microrobots presents great challenges. Therefore, several coil-array-based systems have been developed. In this paper, we develop a magnetic actuation system from both hardware and software aspects for the collaborative motion of multiple magnetic microrobots. The coil structure is optimized to enhance the driving performance of the devised system, and a fused multi-target tracking algorithm is proposed to improve the tracking accuracy. In combination with the CBS algorithm, collision-free paths are planned for multiple identical microrobots. The experimental results show that the constructed system and proposed methods can realize coordinated motion of multiple identical magnetic microrobots, which has enormous potential for some biomedical applications.

Magnetic field-driven microrobots have shown high potential in the field of medical applications. Autonomous navigation is a crucial concern for magnetic microrobots, however, the path planning, actuation and control of magnetic microrobots still remain challenging, especially for complex and large-workspace human body environments. Depending on the specific task and environmental conditions, it is important to employ appropriate planning and control architectures for the magnetic navigation systems. In light of this objective, this paper introduces a novel navigation framework, using an improved path planning and following control method. An evolutionary strategy based RRT (ES-RRT) planner is designed to achieve a shorter, smoother and safer path. Furthermore, an extended state observer (ESO)-based controller is specifically designed for the path tracking process of the microrobots. This controller enables the microrobots to accurately follow the computed path. Experiments demonstrate the effectiveness of the proposed strategy: Feasible path in different conditions and environments can be obtained with short and smooth enough, and autonomous navigation following of microrobots is realized with satisfactory path tracking control accuracy.Note to Practitioners—In contrast to macroscale robots, microrobots face challenges when it comes to integrating onboard components such as processors and power sources. Consequently, alternative methods have been developed, including optical, chemical, and biological actuation. Among these approaches, the utilization of magnetic fields is particularly favorable due to its ability to penetrate deep tissues while ensuring high safety. Additionally, magnetic fields offer diverse propulsion options, such as rotating fields and oscillating fields, along with excellent controllability. Despite significant advancements in the fabrication, functionalization, and locomotion of magnetic microrobots, autonomous navigation remains an area that requires further development. In the medical application, the planning path of the microrobots needs to be short and smooth enough. Besides, considering the unknown dynamics and external disturbances of the system, it is extremely important for microrobots to complete the precise path following control of the planned path. The motivation of this work is to develop an effective navigation scheme for microrobots which consisting of a path planner and motion controller. A shorter and smoother path will be planned based on a novel improved RRT planner. The precise control of the path following then will be achieved by adopting an extended state observer (ESO) controller. The proposed methods would enable microrobots to act safely and greatly enhance robots’ capabilities.

Magnetic microrobots demonstrate significant potential in medical applications by providing innovative solutions for precise treatment through targeted drug delivery, minimally invasive surgery, and vascular cleaning. However, within biological organisms, there are various complex obstacle environments that require a navigation technology prioritizing safety and emphasizing smoothness. This paper proposes a safety-enhanced navigation planning (SENP) algorithm to achieve multiple objectives such as safety, path smoothness, and short distance, enabling collision-free navigation in complex medical environments. Unlike traditional methods that require multiple heuristic cost functions to guide the navigation planning algorithm, our approach leverages the safety-enhancing features of the safe artificial potential field (SAPF) to promote collision-free navigation in complex medical environments. By adopting a goal-biased strategy to guide the generation of random sampling points, the number of iterations is reduced, and the convergence speed of the algorithm is improved. In addition, the traditional artificial potential field planning method often leads to the oscillation of the corridor, our method effectively addresses this issue along with the uncertainty in the quality of the initial path and the lengthy convergence time to the optimal path. Comparative analysis with various algorithms in different environments shows that our proposed method excels in terms of smoothness and path length under the premise of safety, making it suitable for magnetic microrobots in complex environments.Note to Practitioners—The motivation for this work lies in advancing safe, efficient, and rapid navigation strategies for magnetic microrobots in medical applications. While significant progress has been made in the development of magnetic microrobots, navigating through complex environments such as human blood vessels remains a substantial challenge. The ability to perform safe and effective motion planning within narrow and intricate channels is crucial for medical applications. In response to this need, we propose a path planning method specifically designed for magnetically actuated microrobots, based on SENP. Our approach emphasizes achieving a path that is not only short and smooth but also prioritizes safety throughout the navigation process. Compared to traditional sampling-based algorithms, our method effectively overcomes limitations such as initial path quality uncertainty and prolonged convergence to optimal paths. This allows for the rapid generation of a high-quality initial path with a focus on safety while significantly accelerating convergence. Moreover, our approach provides a robust solution for navigating narrow channels, making it highly suitable for challenging medical environments.

Programmable manipulation of fluid-based soft robots has recently attracted considerable attention. Achieving parallel control of large-scale ferrofluid droplet robots (FDRs) is still one of the major challenges that remain unsolved. In this article, we develop a distributed magnetic field control platform to generate a series of localized magnetic fields that enable the simultaneous control of many FDRs, allowing teams of FDRs to collaborate in parallel for multifunctional manipulation tasks. Based on the mathematical model using the finite element method, we first evaluate the distribution properties of the local magnetic fields as well as the gradients generated by individual electromagnets. Meanwhile, the locomotion and deformation behavior of the FDR is also characterized to verify the actuation performance of the developed system. Subsequently, a vision-based closed-loop feedback control strategy is then presented, which aims to achieve path tracking of multiple robot formations. Thermal analysis shows that the system's low output power enables reliable and sustained long-term operation. Finally, the developed system is tested through extensive physical experiments with different numbers of FDRs. The results demonstrate the potential of the designed setup in manipulating dozens of FDRs for digital display, message encoding, and microfluidic logistics. To the best of our knowledge, this is the first attempt that allows independent control of such scale droplet robots (up to 72) for cooperative applications.

The application of microrobots in the biomedical field has attracted great interest, among which drug transportation is one of the application scenarios. Traditional studies used the global magnetic field to control single microrobot, therefore it is impossible to control multiple microrobots. To address this problem, this paper develops a local magnetic field generation system to realize the independent control of multiple microrobots. The proposed multi-microrobot motion system integrates perception, planning, and actuation, enabling autonomous multi-task drug delivery. In our system, we first develop a printed circuit board (PCB) array magnetic driven microrobot system based on a micro coil array, then the Yolov8 framework is employed for the target/environment recognition, accurately identifying microrobots and magnetic fluids, while the Rapidly-exploring Random Trees (RRT) algorithm is used for path planning. We have conducted experiments on obstacle avoidance, droplet transport, and drug fusion. The results clearly demonstrate the significant potential of magnetic fielddriven microcoil array devices in transportation and drug fusion engineering.

Three-dimensional electromagnetic field drive technology, as a cutting-edge remote wireless control method, is extensively utilized in the biomedical diagnosis and treatment of micro-robots. This paper presents the design of a three-dimensional electromagnetic drive system for micro-robots, leveraging a gradient magnetic field to achieve comprehensive automatic control in three axes. Firstly, we refine the iron core’s end structure to produce an uniform gradient magnetic field throughout the three-dimensional space. Following that, the parameters at the end of the iron core are fine-tuned to meet the specifications for magnetic field gradient, magnetic flux density, and effective workspace. Then a three-dimensional electromagnetic drive system with strong magnetic field gradient is established, achieving a remarkable maximum gradient of 1.70 T/m at the center of the workspace. Compared with other systems, the gradient is significantly enhanced. Subsequently, we carry out a three-dimensional drive experiment for a micro-robot, confirming the system’s driving efficacy. To enable precise path following for micro-robots within a three-dimensional space, we have formulated a control strategy rooted in micro-robot dynamics. The controller stability is guaranteed through the Lyapunov theory. Ultimately, a three-dimensional path following experiment is executed on the developed electromagnetic drive system. The experiment confirms the capability of our designed system which can achieve the three-dimensional closed-loop motion for the micro-robot.

In this paper, an integrated framework consisting of the sensing, navigation and control is proposed for an autonomous mobile manipulator driven by series elastic actuators (SEAs) to preform mobile manipulation tasks in unknown environments. First, ORB-SLAM2 technique is combined into the environment sensing by extracting the ORB features, automatic initialization, repositioning and loop detection for real-time posture estimation. Then, the navigation function is designed for generating collision-free trajectory in an environment with obstacles. To realize kinematic and dynamic control of the mobile manipulator with the developed SEA joints, the whole body dynamics is considered and described. And to handle dynamic uncertainties and the SEA inherent saturation limits, a novel adaptive neural network control considering the whole body dynamics is proposed. Without knowing the exact parameters of the whole body model, the designed controller merely requires the position and velocity of the actuators and links, which can make the tracking errors converge to zero and keep all signals uniformly bounded in the closed-loop system. The performance and efficiency of the proposed method are verified by extensive experiments. Note to Practitioners—This paper is motivated by issues of manipulation control of autonomous unmanned system. Traditional manipulation frameworks focus either on sensing or control by assuming that the environment is known, which would result in lacking of autonomy for a specified task. Since mobile manipulation tasks often consist of nonholonomic and holonomic constraints for wheeled mobile manipulators with differential steering. In addition, most current works for whole body control are based on the condition that robot dynamic parameters are known beforehand. Therefore, it is necessary to establish an enhanced framework to simultaneously deal with these problems. In this paper, an integrated navigation and control framework is proposed. To make the mobile manipulator work in the unknown environment, task sensing and whole body control for mobile manipulators are also developed. The framework is partitioned into the task sensing, navigation and control, where the mobile manipulator can fulfill the mobile manipulation tasks in the unstructured environments.

The exoskeleton is mainly used by subjects who suffer muscle injury to enhance motor ability in the daily life environment. Previous research seldom considers extending human collaboration skills to human-robot collaborations. In this article, two models, that is: 1) the following the better model and 2) the interpersonal goal integration model, are designed to facilitate the human-human collaborative manipulation in tracking a moving target. Integrated with dual-arm exoskeletons, these two models can enable the robot to successfully perform target tracking with two human partners. Specifically, the manipulation workspace of the human-exoskeleton system is divided into a human region and a robot region. In the human region, the human acts as the leader during cooperation, while, in the robot region, the robot takes the leading role. A novel region-based Barrier Lyapunov function (BLF) is then designed to handle the change of leader roles between the human and the robot and ensures the operation within the constrained human and robot regions when driving the dual-arm exoskeleton to track the moving target. The designed adaptive controller ensures the convergence of tracking errors in the presence of region switches. Experiments are performed on the dual-arm robotic exoskeleton for the subject with muscle damage or some degree of motor dysfunctions to evaluate the proposed controller in tracking a moving target, and the experimental results demonstrate the effectiveness of the developed control.

Recent advances in fine manipulation have led to increased interest in both scientific research works and engineering applications. Robot manipulation at a level approaching human skills is gaining attention in both industrial and individual services. A major challenge in fine manipulation is the unavoidable uncertainties and unpredictable conditions encountered in dynamic and unstructured application environments. The employment of biologically inspired (bioinspired) embodiments in fine manipulation shows significant advantages in tackling such problems. The aim of bioinspired embodiment is to improve fine manipulation of robotic systems utilizing the knowledge gained from natural systems with biomimetic methods. Such a method includes sensing, planning, and execution. This article provides a comprehensive survey of the current state of bioinspired technologies in fine manipulation, and outlines new challenges and some potential directions.

In this paper, a hierarchical hybrid motion/force control architecture for the manipulation and grasping of mobile manipulators is presented, where the systems are subject to varieties of physical constraints such as Coulomb friction cones, nonholonomic/holonomic constraints, and actuator saturation limits. The incorporation of a projection-based operation space control and an adaptive controller based on the neural networks used in this paper formulates a novel control scheme, so the system stability is further guaranteed and the uncertain dynamics is handled without redesigning the minimal-order dynamics model. Considering the effects of these constraints, the actuator saturation limits are handled by an auxiliary designed system, and the neural dynamics optimization is applied for the quadratically constrained programing problem of the optimal robotic grasping. The dynamic uncertainties can be estimated online by using the developed motion/force control strategy, and the application of a novel disturbance observer is explored to ensure the good tracking performance. The experimental results are presented to verify the performance and the efficiency of the proposed method.