Swarm control is one of the primary challenges in microrobotics. For the automated control of such a microrobotic system with small size and large population, conventional methods using precise robot models and robot–robot communications lose effectiveness due to the complex locomotion of micro/nano agents in a swarm and difficult implementation of onboard actuators and sensors for individual motion control and motion feedback. This article proposes a statistics-based approach and reports the fully automated control of a swarm of paramagnetic nanoparticles including the swarm pattern formation, identification, tracking, motion control, and real-time distribution monitoring/control. By establishing the swarm statistics, collective behaviors of a nanoparticle swarm can be quantitatively analyzed by computers. Algorithms are designed based on the statistics to automatically generate and identify the vortex-like paramagnetic nanoparticle swarm (VPNS), which present robustness to the dose and initial distribution of the nanoparticle swarm. In order to robustly track a VPNS, a statistics-based tracking method is proposed, in which 500 boundary points of the VPNS are extracted and the VPNS distribution is optimally recognized. And, with the proposed gathering improvement control, experiments show that over 70% nanoparticles can be gathered in the VPNS. Furthermore, an automated motion control scheme for the VPNS is proposed which shows high-accuracy trajectory tracking performance (tracking error: <5% body length). Besides, real-time monitoring of the distribution region/density and control of the distribution area for a nanoparticle swarm are also realized by using the statistics. Experimental results validate the feasibility of the proposed method in automated control of paramagnetic nanoparticle swarms.

中文翻译：

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二维空间中大量顺磁性纳米粒子的基于统计的自动控制

群控制是微型机器人的主要挑战之一。对于这种小规模、大人口的微型机器人系统的自动化控制，由于微/纳米代理在群体中的复杂运动以及机载执行器和传感器的难以实现，使用精确机器人模型和机器人-机器人通信的传统方法失去了有效性用于个人运动控制和运动反馈。本文提出了一种基于统计的方法，并报告了对顺磁性纳米粒子群的全自动控制，包括群模式形成、识别、跟踪、运动控制和实时分布监测/控制。通过建立群体统计，计算机可以定量分析纳米粒子群的集体行为。基于统计数据设计算法以自动生成和识别涡旋状顺磁纳米粒子群（VPNS），其对纳米粒子群的剂量和初始分布具有鲁棒性。为了对VPNS进行鲁棒跟踪，提出了一种基于统计的跟踪方法，提取了500个VPNS的边界点，对VPNS分布进行了最优识别。并且，通过提议的收集改进控制，实验表明可以在 VPNS 中收集超过 70% 的纳米粒子。此外，还提出了一种用于 VPNS 的自动运动控制方案，该方案显示出高精度轨迹跟踪性能（跟踪误差：<5% 体长）。除了，利用统计数据也可以实现对纳米粒子群分布区域/密度的实时监测和分布区域的控制。实验结果验证了所提出的方法在顺磁性纳米粒子群自动控制中的可行性。