Cargo capture and transport by colloidal swarms

Abstract

Controlling active colloidal particle swarms could enable useful microscopic functions in emerging applications at the interface of nanotechnology and robotics. Here, we present a computational study of controlling self-propelled colloidal particle propulsion speeds to cooperatively capture and transport cargo particles, which otherwise produce random dispersions. By sensing swarm and cargo coordinates, each particle's speed is actuated according to a control policy based on multiagent assignment and path planning strategies that navigate stochastic particle trajectories to targets around cargo. Colloidal swarms are shown to dynamically cage cargo at their center via inward radial forces while simultaneously translating via directional forces. Speed, power, and efficiency of swarm tasks display emergent coupled dependences on swarm size and pair interactions and approach asymptotic limits indicating near-optimal performance. This scheme exploits unique interactions and stochastic dynamics in colloidal swarms to capture and transport microscopic cargo in a robust, stable, error-tolerant, and dynamic manner.

Cooperative transport by flocking phototactic micromotors

“…Controllable capturing and transporting cargoes such as drug molecules, proteins, capsules, and cells in microenvironments is the pivotal nanotechnology that is extensively studied. [1][2][3][4][5][6] Synthetic micro/nanomotors can extract various energies from the surrounding environment, and convert them into mechanical output allowing for autonomous motions. 7- 15 Thus, the micro/nanomotors are endowed with the undeniable capability to serve as micro/nanocarriers to realize micromanipulation by establishing physical/chemical interactions between the motors and cargoes.…”

Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning

“…More broadly, additional temporal control could enable solutions to problems involving collective dynamics where individuals are precisely controlled to synchronise and coordinate in time (e.g., ants, colloidal swarms). [ 40‐42 ]…”

“…A potential route for improving the performance of translator motors will be exploiting the swarm intelligence, which can be achieved using multi‐agent stochastic feedback control. [ 42 ]…”

Nano/Micromotors in Active Matter

“…Particularly, state-of-the-art learning-based method leverages the learning capacity and generalizability of deep neural networks to derive decision rules that enables robots to navigate in unknown environments. Beyond single NMM, collectively controlled NMM swarms can form intelligent systems that carry out non-trivial tasks such as cargo capture and transport in micro-structured environments [133,134].…”