Leader-Follower 3D Formation for Underwater Robots（面向水下机器人的主从式三维编队）
Leader-Follower 3D Formation for Underwater Robots
AI总结：
针对水下通信难题导致多机器人编队算法难落地的问题，本文提出仅靠视觉感知和低计算反应式控制的主从式三维编队策略，通过BlueSwarm平台首次实验验证三类编队，并结合仿真探究复杂编队的收敛与稳定性，为通信受限的水下机器人群应用奠基。
摘要：
学界假设鱼类的群游行为能带来诸多生存益处，包括觅食成功率提升、躲避捕食者的安全性增强，以及编队游动时通过水动力相互作用节省能量。水下机器人群体在未来的应用场景中或可实现类似优势，例如利用编队控制完成环境监测中的高效空间采样。尽管多机器人编队控制已有大量理论算法，但受水下通信的根本性挑战制约，这些算法尚未在水下领域得到测试。本文提出一种适用于水下编队控制的主从式策略，仅采用基于视觉的感知与低计算量的反应式控制算法，即可实现复杂的三维编队。我们使用物理平台BlueSwarm，首次通过实验验证了串联、并排以及交错游动三种三维编队的实现。我们还在基于物理的模拟器中研究了更复杂的编队形式，为水下惯性/阻力条件下编队的收敛性与稳定性提供了新见解。本研究成果为水下机器人群在通信受限的水生环境中的未来应用奠定了基础。
英文摘要：
The schooling behavior of fish is hypothesized to confer many survival benefits, including foraging success, safety from predators, and energy savings through hydrodynamic interactions when swimming in formation. Underwater robot collectives may be able to achieve similar benefits in future applications, e.g. using formation control to achieve efficient spatial sampling for environmental monitoring. Although many theoretical algorithms exist for multi-robot formation control, they have not been tested in the underwater domain due to the fundamental challenges in underwater communication. Here we introduce a leader-follower strategy for underwater formation control that allows us to realize complex 3D formations, using purely vision-based perception and a reactive control algorithm that is low computation. We use a physical platform, BlueSwarm, to demonstrate for the first time an experimental realization of inline, side-by-side, and staggered swimming 3D formations. More complex formations are studied in a physics-based simulator, providing new insights into the convergence and stability of formations given underwater inertial/drag conditions. Our findings lay the groundwork for future applications of underwater robot swarms in aquatic environments with minimal communication.
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。
