An experimental comparison of path planning techniques applied to micro-sized magnetic agents

Micro-sized agents can be used in applications such as microassembly, micromanipulation, and minimally invasive surgeries. Magnetic agents such as paramagnetic microparticles can be controlled to deliver pharmaceutical agents to difficult-to-access regions within the human body. In order to autonomously move these microparticles toward a target/goal area, an obstacle-free path must be computed using path planning algorithms. Several path planning algorithms have been developed in the literature, however, to the best of our knowledge, only few have been employed in an experimental scenario. In this paper we perform an experimental comparison of six path planning algorithms when applied to the motion control of paramagnetic microparticles. Among the families of deterministic and probabilistic path planners we select the ones that we consider the most fundamental, such as: A* with quadtrees, A* with uniform grids, D* Lite, Artificial Potential Field, Probabilistic Roadmap and Rapidly-exploring Random Tree. We consider a 2D environment made by both dynamic and static obstacles. Four scenarios are evaluated. Three metrics such as computation time, length of the trajectory performed by the microparticle, and time to reach the goal are used to compare the planners. Experimental results reveal equivalence between almost all the considered planners in terms of trajectory length and completion time. Concerning the computation time, A* with quadtrees and Artificial Potential Field achieve the best performances.