Brownian Cargo Capture in Mazes via Intelligent Colloidal Microrobot Swarms

Abstract

Intelligent microrobot systems at the microscopic scale provide enormous opportunities for emerging biomedical and environmental applications. Herein, a multiagent stochastic feedback control framework to control colloidal microrobot swarms for capturing Brownian cargo particles in complex environments such as mazes is proposed. The decision‐making module in the control framework consists of the adaptive generation of target sites surrounding the cargo, optimal target assignment, and approximate motion planning. The stochastic trajectories of robot swarms are efficiently navigated toward their exclusively assigned target around the cargo particle and enable the cargo to be captured. The capture strategy realized by the control framework is robust, adaptive, and flexible in that it accommodates diverse local geometries in the vicinity of a cargo, swarm, and maze sizes and is able to spontaneously split the workforce to catch multiple Brownian cargo particles via multitasking. The present intelligent robot swarm enabled by the multiagent control offers a path to realize complex functions at the microscopic scale in a resilient and flexible manner.

Keywords

• artificial intelligence
• colloidal microrobot swarms
• multiagents
• multitasking