Robots dynamically share LiDAR codes to reduce interference in swarms
Dynamic, Decentralized Spatial Code Reuse for OCDMA LiDAR in Robot Swarms
Robotics
Summary
When many robots use LiDAR sensors to measure distances, their signals can get mixed up, causing interference that makes them less accurate. The authors propose a new way for robots to communicate and quickly change their signal codes based on nearby interference, so fewer unique codes are needed and collisions happen less often. This method works better as the number of robots grows, using far fewer codes than older methods that assign codes once and never change them. Their tests show that coordinating code changes reduces interference significantly, even when robots move around and signals are imperfectly detected.
What this means in practice
- •For robotics engineers: Design LiDAR-equipped robot swarms that dynamically coordinate signal codes to reduce mutual interference and improve ranging accuracy at scale.
- •For wireless system designers: Implement decentralized spatial code reuse protocols to enhance code efficiency and collision reduction in dense optical communication networks.
Authors
Mohammad Hani Alomari
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 $Θ(N)$ 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.