使用强化学习进行微型机器人集群的靶向递送导航
Navigation of micro-robot swarms for targeted delivery using reinforcement learning
AI总结：
本研究使用强化学习算法PPO和RPO，在流体动力学效应下通过单一控制器导航微型游泳者集群至圆形吸收目标，并利用课程学习成功实现了25个游泳者集群的机动控制。
摘要：
微型机器人技术正迅速成为许多医疗治疗方法（特别是靶向药物递送）的一种极具前景的技术解决方案。当它们以集群形式工作时非常有效，但由于其尺寸微小，对单个机器人的控制通常是不可行的。因此，使用单一控制器控制多个机器人非常重要，而人工智能可以帮助我们成功完成这项任务。在本研究中，我们使用强化学习（RL）算法近端策略优化（PPO）和鲁棒策略优化（RPO），在流体动力学效应下，通过控制朝向来导航由4、9和16个微型游泳者组成的集群，使其到达一个圆形吸收目标。我们考察了PPO和RPO在有限状态信息场景下的性能，并测试了它们在目标位置和大小随机变化时的鲁棒性。我们使用课程学习来提升性能，并在学习导航由25个游泳者组成的集群以及引导集群的过程中展示了这一点，以此例证RL模型的机动能力。
英文摘要：
Micro robotics is quickly emerging to be a promising technological solution to many medical treatments with focus on targeted drug delivery. They are effective when working in swarms whose individual control is mostly infeasible owing to their minute size. Controlling a number of robots with a single controller is thus important and artificial intelligence can help us perform this task successfully. In this work, we use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers under hydrodynamic effects, controlled by their orientation, towards a circular absorbing target. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size. We use curriculum learning to improve upon the performance and demonstrate the same in learning to navigate a swarm of 25 swimmers and steering the swarm to exemplify the manoeuvring capabilities of the RL model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
