Statistics-Based Automated Control for a Swarm of Paramagnetic Nanoparticles in 2-D Space

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.