LAUSR

Nanomanipulation plays a significant role in nanotechnology research. The process of Atomic force microscopy (AFM) based manipulation is complex and time-consuming, which can be improved using a path-planning algorithm to reduce its manipulation time and time complexity. Due to real-time monitoring limitation in AFM based manipulations, Virtual reality (VR) environments have been developed. One such developed VR environment, however, is limited to point to point manipulation and lacks any path information. Therefore, we propose using a hybrid Improved particle swarm optimization (IPSO), a cellular automata-based algorithm for path planning during manipulation of micro/nanoparticles. In this technique, the critical time-force diagram, representing the AFM based manipulation dynamic is considered as a constraint, and is subsequently used to find the best path. The main path is divided into several segments and is optimized. Used as an algorithm for manipulation, this technique provides a more precise path in the AFM-based manipulation. Finally, the ability of this technique was compared to the other path planner algorithms based on its efficiency in reducing time-complexity parameters.