论文标题
微型/纳米电动机导航和通过深度强化学习的本地化
Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning
论文作者
论文摘要
在复杂的景观中，布朗微/纳米自行车运动颗粒的有效导航和精确定位可以使未来的高科技应用涉及涉及药物输送，精确手术，石油回收和环境补救。在这里，我们采用基于生物启发的神经网络的无模型深钢筋学习算法，以使不同类型的微型/纳米电动机能够连续控制以执行复杂的导航和本地化任务。发现具有可调的自传速度或方向或两者的微型/纳米电动机表现出明显不同的动力学。特别是，需要独特的控制策略来在自由空间和障碍环境以及时间限制下实现有效的导航。我们的发现提供了对使用人工智能控制的布朗颗粒的主动动态的基本见解，并可以指导具有不同应用要求的电机和机器人控制系统的设计。
Efficient navigation and precise localization of Brownian micro/nano self-propelled motor particles within complex landscapes could enable future high-tech applications involving for example drug delivery, precision surgery, oil recovery, and environmental remediation. Here we employ a model-free deep reinforcement learning algorithm based on bio-inspired neural networks to enable different types of micro/nano motors to be continuously controlled to carry out complex navigation and localization tasks. Micro/nano motors with either tunable self-propelling speeds or orientations or both, are found to exhibit strikingly different dynamics. In particular, distinct control strategies are required to achieve effective navigation in free space and obstacle environments, as well as under time constraints. Our findings provide fundamental insights into active dynamics of Brownian particles controlled using artificial intelligence and could guide the design of motor and robot control systems with diverse application requirements.
