用强化学习设计微小泳者自适应游动轨迹，可追踪复杂路径和移动目标。
Navigation of a Three-Link Microswimmer via Deep Reinforcement Learning
- 用强化学习设计两种游动策略：提速优先与节能兼顾。
- 不同奖励函数生成不同游动模式，优于传统优化方法。
- 能动态调整游动方式，适合复杂环境下的智能微机器导航。
运动微生物在复杂生物环境中发展出高效的游动步态。将这种适应性应用于智能微机器人，在运动规划与游动设计方面面临重大挑战。本文探索利用强化学习（RL）为低雷诺数下的三连杆微泳者模型设计定向导航的游动模式。具体设计了两种基于RL的策略：一种侧重最大化速度（速度优先策略），另一种兼顾速度与能耗（节能感知策略）。结果表明，不同奖励函数会影响RL生成的游动模式，且其性能优于传统优化方法。此外，展示了强化学习驱动的泳者在执行多种导航任务时的自适应能力，包括追踪复杂轨迹和追逐移动目标。综上，本工作凸显了强化学习在设计高效、自适应微泳者方面的潜力，使其可在复杂环境中完成复杂机动。
原文摘要 · Abstract (English)
Motile microorganisms develop effective swimming gaits to adapt to complex biological environments. Translating this adaptability to smart microrobots presents significant challenges in motion planning and stroke design. In this work, we explore the use of reinforcement learning (RL) to develop stroke patterns for targeted navigation in a three-link swimmer model at low Reynolds numbers. Specifically, we design two RL-based strategies: one focusing on maximizing velocity (Velocity-Focused Strategy) and another balancing velocity with energy consumption (Energy-Aware Strategy). Our results demonstrate how the use of different reward functions influences the resulting stroke patterns developed via RL, which are compared with those obtained from traditional optimization methods. Furthermore, we showcase the capability of the RL-powered swimmer in adapting its stroke patterns in performing different navigation tasks, including tracing complex trajectories and pursuing moving targets. Taken together, this work highlights the potential of reinforcement learning as a versatile tool for designing efficient and adaptive microswimmers capable of sophisticated maneuvers in complex environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。