Dynamic, Decentralized Spatial Code Reuse for OCDMA LiDAR in Robot Swarms
Abstract
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 \(\Theta(N)\) 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.
TL;DR
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.