发表机构
German Jordanian University（德国约旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种去中心化动态空间码复用协议，基于干扰邻域图实现码数O(log N/log log N)增长，相比静态分配Θ(N)有显著改进，并在模拟中验证了更高的码复用效率和更低的碰撞风险。
AI 中文摘要
配备激光雷达的机器人群体中，当光学测距码发生碰撞时，机器人会相互干扰。现有的缓解措施要么静态分配码——对于N个机器人需要L=N个可区分的码——要么对检测到的干扰做出反应，但缺乏可扩展的、协调的分配规则作为其基础；先前的工作明确将码分配扩展问题视为未解决。我们提出一种去中心化协议，其中机器人基于实时信标维护的干扰邻域图动态重新分配空间复用码，并证明在恒定机器人密度下所需码数按O(log N/log log N)增长——相比静态分配的Θ(N)增长，这是一个无界改进。我们通过蒙特卡洛模拟（每种条件30个种子，95%置信区间）在远超理想化证明的条件下验证了这一结果——包括机器人移动性、基于信标的非完美检测以及反应式重新分配：与静态分配相比，优势从15个机器人时的约2倍扩大到120个机器人时的12倍。针对现有无协调方法的结构忠实、公平构建的模型，我们的协议在相同受限码预算下实现了显著更高的码复用效率和30-40%更低的碰撞风险，证明协调——而非仅反应——才是缩小扩展差距的关键。
英文摘要
Robots in a LiDAR-equipped swarm mutually interfere when their optical ranging codes collide. Existing mitigations either assign codes statically -- requiring $L=N$ distinguishable codes for $N$ robots -- or react to detected interference without a scalable, coordinated assignment rule beneath them; prior work explicitly identifies the code-assignment scaling problem as unsolved. We propose a decentralized protocol in which robots dynamically reassign spatial reuse codes based on a live, beacon-maintained interference-neighborhood graph, and prove that the number of codes required grows as $O(\log N/\log\log N)$ under constant robot density -- an unbounded improvement over the $Θ(N)$ growth of static assignment. We validate this result under conditions substantially beyond the idealized proof -- robot mobility, imperfect beacon-based detection, and reactive reassignment -- via Monte Carlo simulation (30 seeds per condition, 95% confidence intervals): the advantage over static assignment widens from roughly $2\times$ at 15 robots to $12\times$ at 120. Against a structurally faithful, fairly constructed model of an existing coordination-free approach, our protocol achieves both substantially greater code-reuse efficiency and 30--40% lower collision risk under an identical, constrained code budget, demonstrating that coordination -- not merely reactivity -- is what closes the scaling gap.
