Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems

Networked robotics systems often work in collaboration to accomplish tasks. The random environments the robots work in render any previous contact data between robots useless as the contact patterns are different for each deployment. In the case of military and disaster scenarios, delivering data items quickly is imperative to the success of a mission. However, robots have limited battery and need a lightweight protocol that maximizes data delivery ratio and minimizes data delivery latency while consuming minimal energy. We present two learning automata based data dissemination protocols, LADD and sc-LADD. LADD uses learning automata with direct connections to all neighboring nodes to make efficient and accurate forwarding decisions while sc-LADD uses learning automata and exploits the clustering nature of the robotic systems to abstract clusters/groups and reduce the number of decisions available to the learning automata, which also reduces overhead.