Guidance Scheme of Magnetic Nanoparticles with Artificial Potential Field

Targeted drug delivery (TDD) is performed using various different technologies such as micro-robots, encapsulation and nanoparticles. Among them, magnetic drug targeting has been developed as a plausible approach to significantly improve the efficiency of drug delivery. Although there have been many researches on the guidance of nanoparticles to a target area, it is still difficult to control the nanoparticles using magnetic force due to their small size, which makes their movements highly dependent on the environmental factors such as fluid flow. Therefore, this paper proposes a guidance scheme for nanoparticles using the artificial potential field, which is a powerful method used for path planning of mobile robots. Through this method, if the particles' initial positions and velocities are randomized; we can obtain an optimized trajectory by updating each particle's position vector. Moreover, by implementing the potential field as a magnetic force, the particle trajectory obtained through the simulation can be implemented in a real experiment. In the current research we have presented only an open loop control scheme. However, if we use a feedback device with a high enough resolution, for instance Magnetic Particle Imaging (MPI), the suggested scheme can enhance significantly the efficiency of drug delivery.