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.

Non-prehensile manipulation methods usually use a simple end effector, e.g., a single rod, to manipulate the object. Compared to the grasping method, such an end effector is compact and flexible, and hence it can perform tasks in a constrained workspace; As a trade-off, it has relatively few degrees of freedom (DoFs), resulting in an under-actuation problem with complex constraints for planning and control. This paper proposes a new non-prehensile manipulation method for the task of object retrieval in cluttered environments, using a rod-like pusher. Specifically, a candidate trajectory in a cluttered environment is first generated with an improved Rapidly-Exploring Random Tree (RRT) planner; Then, a Model Predictive Control (MPC) scheme is applied to stabilize the slider's poses through necessary contact with obstacles. Different from existing methods, the proposed approach is with the contact-aware feature, which enables the synthesized effect of active removal of obstacles, avoidance behavior, and switching contact face for improved dexterity. Hence both the feasibility and efficiency of the task are greatly promoted. The performance of the proposed method is validated in a planar object retrieval task, where the target object, surrounded by many fixed or movable obstacles, is manipulated and isolated. Both simulation and experimental results are presented.

Laser-driven micro-tools are formulated by treating highly-focused laser beams as actuators, to control the tool's motion to contact then manipulate a micro object, which allows it to manipulate opaque micro objects, or large cells without causing photodamage. However, most existing laser-driven tools are limited to relatively simple tasks, such as moving and caging, and cannot carry out in-hand dexterous tasks. This is mainly because in-hand manipulation involves continuously coordinating multiple laser beams, micro-tools, and the object itself, which has high degrees of freedom (DoF) and poses up challenge for planner and controller design. This paper presents a new hierarchical formulation for the grasping and manipulation of micro objects using multiple laser-driven micro-tools. In hardware, multiple laser-driven tools are assembled to act as a robotic hand to carry out in-hand tasks (e.g., rotating); in software, a hierarchical scheme is developed to shrunken the action space and coordinate the motion of multiple tools, subject to both the parametric uncertainty in the tool and the unknown dynamic model of the object. Such a formulation provides potential for achieving robotic in-hand manipulation at a micro scale. The performance of the proposed system is validated in simulation studies under different scenarios.