Targeted drug delivery in the gastrointestinal (GI) tract using magnetic robots offers a promising alternative to systemic treatments. However, controlling these robots is a major challenge. Stationary magnetic systems have a limited workspace, while mobile systems (e.g., coils on a robotic arm) suffer from a "model-calibration bottleneck", requiring complex, pre-calibrated physical models that are time-consuming to create and computationally expensive. This paper presents a compact, low-cost mobile magnetic manipulation platform that overcomes this limitation using Deep Reinforcement Learning (DRL). Our system features a compact four-electromagnet array mounted on a UR5 collaborative robot. A Soft Actor-Critic (SAC)-based control strategy is trained through a sim-to-real pipeline, enabling effective policy deployment within 15 minutes and significantly reducing setup time. We validated the platform by controlling a 7-mm magnetic capsule along 2D trajectories. Our DRL-based controller achieved a root-mean-square error (RMSE) of 1.18~mm for a square path and 1.50~mm for a circular path. We also demonstrated successful tracking over a clinically relevant, 30 cm * 20 cm workspace. This work demonstrates a rapidly deployable, model-free control framework capable of precise magnetic manipulation in a large workspace,validated using a 2D GI phantom.

翻译：利用磁性机器人实现胃肠道靶向给药为全身性治疗提供了一种前景广阔的替代方案。然而，控制这些机器人是一项重大挑战。固定式磁系统工作空间有限，而移动式系统（例如安装在机械臂上的线圈）则存在“模型校准瓶颈”，需要复杂且需预先校准的物理模型，这些模型不仅创建耗时，计算成本也高。本文提出了一种紧凑、低成本的移动磁操控平台，该平台利用深度强化学习克服了这一限制。我们的系统采用安装在UR5协作机器人上的紧凑型四电磁铁阵列。通过仿真到现实的训练流程，训练了一种基于柔性演员-评论家算法的控制策略，使得有效策略部署可在15分钟内完成，显著减少了设置时间。我们通过控制一个7毫米磁性胶囊沿二维轨迹运动验证了该平台。我们基于深度强化学习的控制器在方形路径上实现了1.18毫米的均方根误差，在圆形路径上实现了1.50毫米的均方根误差。我们还展示了在临床相关的30厘米×20厘米工作空间内成功进行跟踪。这项工作展示了一种可快速部署、无模型的控制框架，能够在较大工作空间内实现精确磁操控，并使用二维胃肠道模型进行了验证。