首页|IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping

IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping

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  • NETL
  • NSTL
  • IEEE
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.

RobotsEavesdroppingKnowledge transferMemory managementScalabilityRobot kinematicsPlanningOntologiesTranslationTraining

Sanjay Sarma Oruganti Venkata、Ramviyas Parasuraman、Ramana Pidaparti

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Cognitive Science Department, Rensselaer Polytechnic Institute, Troy, NY, USA|School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA

School of Computing, University of Georgia, Athens, GA, USA

School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA, USA

2025

10.1109/TCYB.2025.3560564

IEEE transactions on cybernetics

IEEE transactions on cybernetics

ISSN：
年,卷(期)：2025.55(6)
  • 33