Motion planning for paramagnetic microparticles under motion and sensing uncertainty

Paramagnetic microparticles moving through fluids have the potential to be used in many applications, including microassembly, micromanipulation, and highly localized delivery of therapeutic agents inside the human body. Paramagnetic microparticles with diameters of approximately 100 μm can be wirelessly controlled by externally applying magnetic field gradients using electromagnets. In this paper, we introduce a motion planner to guide a spherical paramagnetic microparticle to a target while avoiding obstacles. The motion planner explicitly considers uncertainty in the microparticle´s motion and maximizes the probability that the microparticle avoids obstacle collisions and reaches the target. To enable effective consideration of uncertainty, we use an Expectation Maximization (EM) algorithm to learn a stochastic model of the uncertainty in microparticle motion and state sensing from experiments conducted in a 3D 8-electromagnet microparticle testbed. We apply the motion planner in a simulated 3D environment with static obstacles and demonstrate that the computed plans are more likely to result in task success than plans based on traditional metrics such as shortest path or maximum clearance.