Chen Zhihan, Huang Siyuan, Zheng Yuebing
Materials Science & Engineering Program and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA.
Materials Science & Engineering Program and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712, USA; Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Adv Intell Syst. 2024 Dec;6(12). doi: 10.1002/aisy.202400409. Epub 2024 Sep 19.
The local force field generated by light endows optical microrobots with remarkable flexibility and adaptivity, promising significant advancements in precise medicine and cell transport. Nevertheless, the automated navigation of multiple optical microrobots in intricate, dynamic environments over extended distances remains a challenge. In this study, we introduce a versatile control strategy aimed at navigating optical microrobotic swarms to distant targets under obstacles of varying sizes, shapes, and velocities. By confining all microrobots within a manipulation domain, we ensure swarm integrity while mitigating the effects of Brownian motion. Obstacle's elliptical approximation is developed to facilitate efficient obstacle avoidance for microrobotic swarms. Additionally, we integrate several supplementary functions to enhance swarm robustness and intelligence, addressing uncertainties such as swarm collapse, particle immobilization, and anomalous laser-obstacle interactions in real microscopic environments. We further demonstrate the efficacy and versatility of our proposed strategy by achieving autonomous long-distance navigation to a series of targets. This strategy is compatible with both optical trapping- and nudging-based microrobotic swarms, representing a significant advancement in enabling optical microrobots to undertake complex tasks such as drug delivery and nanosurgery and understanding collective motions.
光产生的局部力场赋予光学微型机器人显著的灵活性和适应性,有望在精准医学和细胞运输方面取得重大进展。然而,多个光学微型机器人在复杂、动态环境中进行长距离自动导航仍然是一项挑战。在本研究中,我们引入了一种通用控制策略,旨在引导光学微型机器人群在大小、形状和速度各异的障碍物存在的情况下到达远处目标。通过将所有微型机器人限制在一个操作域内,我们在减轻布朗运动影响的同时确保了群体的完整性。开发了障碍物的椭圆近似方法,以促进微型机器人群高效避障。此外,我们集成了几个补充功能来增强群体的鲁棒性和智能性,解决实际微观环境中的群体崩溃、粒子固定以及异常激光 - 障碍物相互作用等不确定性问题。我们通过实现对一系列目标的自主长距离导航,进一步证明了我们提出的策略的有效性和通用性。该策略与基于光镊和微推的微型机器人群均兼容,代表了在使光学微型机器人能够承担诸如药物递送和纳米手术等复杂任务以及理解集体运动方面的重大进展。
Adv Intell Syst. 2024-12
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