While magnetic micro-robots have demonstrated significant potential across various applications, including drug delivery and microsurgery, the open issue of precise navigation and control in complex fluid environments is crucial for in vivo implementation. This paper introduces a novel flow-aware navigation and control strategy for magnetic micro-robots that explicitly accounts for the impact of fluid flow on their movement. First, the proposed method employs a Physics-Informed U-Net (PI-UNet) to refine the numerically predicted fluid velocity using local observations. The predicted velocity is then incorporated into a flow-aware A* path planning algorithm, ensuring efficient navigation while mitigating flow-induced disturbances. Finally, a control scheme is developed to compensate for the predicted fluid velocity, thereby optimizing the micro-robot’s performance. A series of simulation studies and real-world experiments are conducted to validate the efficacy of the proposed approach. This method enhances both planning accuracy and control precision, expanding the potential applications of magnetic micro-robots in fluid-affected environments typical of many medical scenarios.

The solution exchange process is crucial in cell cryopreservation, an assistive reproductive technique that enhances reproductive autonomy and helps women overcome infertility challenges. Such a task is time-critical in the sense that the duration of cell exposure to the solutions significantly impacts cell viability. In this paper, a new micromanipulation system has been developed to automate such a task, where the contributions can be summarized as follows. This paper addresses the challenge of tracking cell positions within a microfluidic chip, due to the limitations of the microscope’s field of view (FOV). By utilizing a region control approach with visual feedback, the proposed method ensures that the cell remains centered in the image. Additionally, a real-time tracking method based on correlation filtering is presented, which precisely localizes cells and controls the syringe pump flow rate to mitigate delays and prevent cell loss. Experimental results illustrate the consistent positioning of the micro objects/cells at each step, the satisfactory success rate, and the robustness to the loss of vision feature. Integrated with novel manipulation strategies, our intelligent manipulation system offers a promising solution for in vitro fertilization (IVF), characterized by an embryologist-centered configuration and standardized robotic manipulation. This study aims to standardize clinical cryopreservation by transitioning from manual operation to a more efficient and reliable automated process. Furthermore, this approach can simplify procedures and enhance oocyte viability. Note to Practitioners—The motivation for this study was to propose an automatic method to address the challenges of manual cell cryopreservation, such as high operational complexity, low efficiency, and significant cell damage. Existing automated cell cryopreservation methods have shortcomings as they fail to simultaneously achieve comprehensive cell tracking and avoid damage caused by direct contact between cells and microtools. Additionally, the high complexity of robotic operations makes clinical application difficult. To overcome these limitations, this study introduces a novel robotic micromanipulation method based on microfluidic chip technology. This method enables full-process cell tracking, FOV tracking of the motorized stage, and non-contact manipulation during the exchange process of cells and solutions. Experimental results showed that the proposed method outperformed existing manual methods in terms of oocyte viability. For fertility experts and medical staff, such a robotic micromanipulation system can be combined with other automatic machines to provide a solution to cell surgery, achieving higher accuracy and efficiency.

Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object, or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this article proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

Embryos/oocytes vitrification is an essential cryopreservation technique in IVF (in vitro fertilization) clinics. The reliable and effective transferring of embryos/oocytes is crucial to the subsequent steps in the whole procedure of vitrification. After each transferring, the straw needs to be replaced with a new one. Due to the uncertainties in the fabrication and installation, the exact knowledge of the kinematic model of the straw is usually unknown, and the relationship between the microscope and the straw is also unknown without calibration beforehand. In such situation, automatically transferring the oocytes from micropipette to the narrow tip of straw (0.7mm) is very challenging. In this paper, a new vision-guided robotic system is developed to automate the transferring of the oocyte without calibration. To this end, the unknown depth information is estimated then compensated by constructing a deep vision network through microscope image, and an approximate Jacobian control algorithm is also proposed to servo control the end tip of the uncalibrated straw to contact the micropipette with the vision feedback. After that, the oocyte is automatically transferred from the micropipette to the straw to finalize the task. The stability of the closed-loop control system is rigorously proved with Lyapunov methods, and the effectiveness of the developed robot is validated in experiments.