Brownian Cargo Capture in Mazes via Intelligent Colloidal Microrobot Swarms

Kun Xu, Yuguang Yang, Bo Li
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引用次数: 7

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.
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通过智能胶体微机器人群在迷宫中捕获布朗货物
微观尺度的智能微型机器人系统为新兴的生物医学和环境应用提供了巨大的机会。为此,提出了一种多智能体随机反馈控制框架,用于控制胶体微机器人在迷宫等复杂环境中捕获布朗货物颗粒。控制框架中的决策模块包括货物周围目标位置的自适应生成、最优目标分配和近似运动规划。机器人群的随机轨迹有效地导航到它们在货物粒子周围的唯一指定目标,并使货物能够被捕获。控制框架实现的捕获策略具有鲁棒性、适应性和灵活性,因为它可以适应货物、群体和迷宫大小附近的不同局部几何形状,并且能够自发地拆分劳动力,通过多任务处理捕获多个布朗货物颗粒。基于多智能体控制的智能机器人群为在微观尺度上以弹性和柔性的方式实现复杂功能提供了途径。
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