水下机器人用视觉实现3D编队，低算力高效稳定。
Leader-Follower 3D Formation for Underwater Robots
- 纯视觉感知+反应式控制，无需复杂通信
- 首次实现实体水中直线、并列、错位3D编队
- 适合无通信或弱通信环境的水下探测任务
鱼类集群行为被认为能带来觅食优势、避敌安全和游泳时通过流体相互作用节能。未来水下机器人集体可能通过编队控制实现类似效益，例如在环境监测中高效进行空间采样。尽管已有大量多机器人编队控制理论算法，但因水下通信基础性挑战，尚未在真实水下环境中验证。本文提出一种基于视觉的领导者-跟随者编队策略，可实现复杂3D编队，采用低计算量的反应式控制算法。使用物理平台BlueSwarm，首次实现在水下实现直线、并列、错位三种3D编队。更复杂的编队在基于物理的仿真器中研究，揭示了在水下惯性/阻力条件下编队收敛与稳定性的新规律。研究成果为未来无通信或低通信环境下水下机器人集群应用奠定基础。
原文摘要 · Abstract (English)
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.
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。