When Discovery Becomes a Storm: A ROS 2 Discovery Model for Wireless Robotic Networks
2026-08-03 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors study how robots using ROS 2 find each other to share information wirelessly. They found that delays and lost messages cause retries that make the wireless network busier, leading to a feedback loop called a 'discovery storm' that slows down connections. To understand this, they created a new mathematical model that shows how these delays cause more messages and longer discovery times. They tested the model extensively and showed it predicts real behavior better than previous methods. Using insights from their model, they designed a smarter discovery process that makes robot connections faster by about 25-40%.
ROS 2Data Distribution Servicediscovery protocolwireless communicationretransmissionchannel contentionfeedback loopanalytical modelingdiscovery completion timereliability timers
Authors
Yeonwoo Choi, Sanghoon Lee, Kyung-Joon Park
Abstract
In Robot Operating System 2 (ROS 2), Data Distribution Service (DDS) participants must discover one another before exchanging data. In wireless environments, delayed or lost discovery messages cause reliability timers to expire, triggering retransmissions that intensify channel contention and further delay the delivery of discovery messages. This self-reinforcing feedback can escalate into a discovery storm. Existing models characterize discovery demand under fixed delivery conditions, but do not capture how shared-channel delay changes protocol state and generates further traffic. To address this issue, we present the first closed-loop analytical model of ROS 2 discovery that characterizes how delay-induced feedback amplifies retransmission overhead and leads to severe discovery storms. Our model represents channel contention as a shared service process, coupling message-delivery latency with receiver states and reliability timers. The model predicts both discovery completion time and per-class message counts. We validate the model through 1,350 experimental runs across 90 topology configurations. An open-loop airtime baseline captures only a fraction of the high-load completion time. The closed-loop model reproduces this rise and conservatively upper-bounds the observed high-load range. Guided by insights from the model, we further design a response-aware discovery policy that reduces mean discovery completion time by 25.3% to 39.7%.