Yang Yuguang, Bevan Michael A
Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Sci Adv. 2020 Jan 24;6(4):eaay7679. doi: 10.1126/sciadv.aay7679. eCollection 2020 Jan.
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.
控制活性胶体粒子群能够在纳米技术与机器人技术交叉的新兴应用中实现有用的微观功能。在此，我们进行了一项计算研究，通过控制自驱动胶体粒子的推进速度来协同捕获和运输货物粒子，否则这些货物粒子会产生随机分散。通过感知群体和货物坐标，每个粒子的速度根据基于多智能体分配和路径规划策略的控制策略进行驱动，该策略将随机粒子轨迹引导至货物周围的目标。研究表明，胶体粒子群通过向内的径向力在其中心动态地围住货物，同时通过定向力进行平移。群体任务的速度、功率和效率表现出对群体大小和成对相互作用的新兴耦合依赖性，并接近渐近极限，表明性能接近最优。该方案利用胶体粒子群中独特的相互作用和随机动力学，以稳健、稳定、容错和动态的方式捕获和运输微观货物。
