基于通信窃听的多机器人系统间接知识转移行为树框架

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Sanjay Sarma Oruganti Venkata;Ramviyas Parasuraman;Ramana Pidaparti
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引用次数: 0

摘要

多智能体和多机器人系统(MRS)通常依赖于直接通信来实现信息共享。这项工作探索了一种受窃听机制启发的替代方法,该方法涉及对代理交互的随意观察,以增强分散的知识传播。我们通过为基于行为的MRS量身定制的行为树(IKT-BT)框架实现了这一目标,该框架将知识和控制动作封装在行为树(BT)中。我们提出了两种新的基于bp的窃听-更新(EU)和窃听-缓冲-更新(EBU)模式,它们结合了独特的窃听策略和有效的情景记忆管理,适用于资源有限的群体机器人。我们从理论上分析了用于MRS的IKT-BT框架,并通过模拟搜索和救援任务的广泛实验验证了所提出模式的性能。我们的研究结果表明,在减少直接沟通需求的情况下,全球任务绩效结果和代理级知识传播都有所改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping
Multiagent and multirobot systems (MRS) often rely on direct communication for information sharing. This work explores an alternative approach inspired by eavesdropping mechanisms in nature that involves casual observation of agent interactions to enhance decentralized knowledge dissemination. We achieve this through a novel indirect knowledge transfer through behavior trees (IKT-BT) framework tailored for a behavior-based MRS, encapsulating knowledge and control actions in behavior trees (BT). We present two new BT-based modalities—eavesdrop-update (EU) and eavesdrop-buffer-update (EBU)—incorporating unique eavesdropping strategies and efficient episodic memory management suited for resource-limited swarm robots. We theoretically analyze the IKT-BT framework for an MRS and validate the performance of the proposed modalities through extensive experiments simulating a search and rescue mission. Our results reveal improvements in both global mission performance outcomes and agent-level knowledge dissemination with a reduced need for direct communication.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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