Modeling nanorobot control using swarm intelligence for blood vessel repair: A rigid-tube model

Abstract

The future nanorobots for diagnosis and treatment purposes in nanomedicine may exhibit only simple behaviors and work together in their early stage. Through exploring the existing swarm intelligence techniques, the canonical particle swarm optimization was selected to employ for adaptively controlling the locomotion of a swarm system of early-stage nanorobots with only essential characteristics for self-assembly into a structure in a simulations system. In this study, we demonstrated nanorobots operating as artificial platelets for repairing wounds in a simulated human small vessel, which may be used to treat platelet diseases. In a rigid-tube model, we investigated how artificial platelet capabilities including the perception range, maximum velocity and response speed have impacts on wound healing effectiveness. It was found that the canonical particle swarm optimization is an efficient algorithm for controlling the early-stage nanorobots with essential characteristics in both Newtonian and non-Newtonian flow models. The demonstration could be beneficial as guidelines of essential characteristics and useful locomotion control for the realization of nanorobots for medical applications in the future.