Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems
Distributed Learning Automata Based Data Dissemination in Networked Robotic Systems
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DOI：
10.1007/978-3-030-28468-8_10
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Gerald Henderson;Qi Han
中科院分区：
文献类型：
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作者：
Gerald Henderson;Qi Han
网络机器人系统通常协作完成任务。机器人工作的随机环境使得机器人之间的任何先前的接触数据都是无用的，因为接触模式对于每个部署都是不同的。在军事和灾难场景中，快速交付数据项对于使命的成功至关重要。然而，机器人的电池有限，需要一个轻量级的协议，最大限度地提高数据传输率，并最大限度地减少数据传输延迟，同时消耗最少的能量。我们提出了两个基于学习自动机的数据分发协议，拉德和SC-LADD。拉德使用与所有相邻节点直接连接的学习自动机来做出有效和准确的转发决策，而sc-LADD使用学习自动机并利用机器人系统的聚类性质来抽象集群/组，并减少学习自动机可用的决策数量，这也降低了开销。
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.