Path planning in the AFM nanomanipulation of multiple spherical nanoparticles by using a coevolutionary Genetic Algorithm

Recently, Atomic Force Microscope (AFM) based nanorobots have emerged as the ultimate tool for assembling nanostructures. The main obstacle in precise nano-manipulation is the lack of real-time monitoring; therefore, computer simulations have been employed to surmount this limitation. In this paper, an algorithm has been proposed to maximize the effectiveness of the existing computer simulations. The existing simulations only deal with one-dimensional particle paths and disregard the existence of other particles as well as surface roughness. In this paper, a more realistic path, which considers the presence of other particles and surface roughness, has been proposed. The Artificial Potential Field Method has been applied to find the initial sub-optimal paths. Then, a customized Genetic Algorithm has been used to find the optimal solution for the path planning problem. The main goal is to minimize the manipulation time while keeping the applied force within the desired limits and make the nano assembly more practical.