Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning

Abstract

Equipping micro‐/nanoscale colloidal robots with artificial intelligence (AI) such that they can efficiently navigate in unknown complex environments can dramatically impact their use in emerging applications such as precision surgery and targeted nanodrug delivery. Herein, a model‐free deep reinforcement learning algorithm is developed that trains colloidal robots to efficiently navigate in unknown environments with random obstacles. A deep neural network architecture is used that enables the colloidal robots to mimic animal navigation decision‐making by directly processing raw sensor input and decomposing long‐range navigations to short‐range ones. The trained robot agents learn to make navigation decisions regarding both obstacle avoidance and travel time minimization, based solely on local sensory inputs without prior knowledge of the global environment. Such agents with biologically inspired mechanisms can acquire competitive navigation capabilities in large‐scale, complex environments containing obstacles of diverse shapes, sizes, and configurations. Herein, the potential of AI to enable colloidal robots in extensive applications is illustrated.