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.

翻译：磁性微型机器人可在外加磁场引导下，自主在复杂非结构化生物体内环境中运动，潜在应用涵盖药物递送、诊断及治疗干预。现有技术通常赋予目标物体磁性，或驱动机器人接触目标物体后实施操控，二者均可能引发物理损伤。本文提出一种非接触操控方案：机器人通过自旋产生排斥场，在不发生物理接触的情形下推动目标物体。该方案的核心挑战在于，磁场输入与目标物体输出速度间的运动模型通常未知且难以解析。为此，本文提出基于数据驱动的解决方案：构建神经网络高效估计运动模型，继而开发基于近似模型的最优控制策略，在满足非接触距离约束条件下推动目标物体跟踪时变轨迹。此外，引入简洁的规划器评估非接触操控在杂乱非结构化环境中的适应性。实验结果展示了所提方案在轨迹跟踪与自主导航方面的性能优势。