A Multi-robot Navigation Framework using Semantic Knowledge for Logistics Environment

In this paper, we introduce the semantic navigation framework for Multi-Robot Systems (MRS). In order for a robot to understand a complex environment, it is necessary for it to comprehend the surrounding environment like a human. Therefore, We have adapted the single robot framework from previous re...

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Bibliographic Details
Main Authors: Choi, Jun-Hyeon, Bae, Sang-Hyeon, Gilberto, Galvis Giraldo, Seo, Dong-Su, Kwon, Seung-Won, Kwon, Gi-Hyeon, Ahn, Ye-Chan, Joo, Kyeong-Jin, Kuc, Tae-Yong
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Automation
Autonomous navigation
Autonomous robots
Multi-robot planning
Multi-robot system
Navigation
Ontologies
Ontology
Planning
Robot sensing systems
Semantic navigation
Semantics
Space vehicles
Virtual environments
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Summary:In this paper, we introduce the semantic navigation framework for Multi-Robot Systems (MRS). In order for a robot to understand a complex environment, it is necessary for it to comprehend the surrounding environment like a human. Therefore, We have adapted the single robot framework from previous research to be suitable for MRS. The proposed framework consists of Semantic Modeling Framework (SMF), Semantic Autonomous Navigation (SAN), Semantic Information Processing (SIP), and Multi-Robot Task Planner. SMF represents the surrounding environment as a topological graph using Triplet Ontology Semantic Model (TOSM), providing essential information for autonomous navigation planning and processing. SAN is an autonomous navigation module that executes actions based on sequences generated by the Multi-Robot Task Planner. SIP processes information to determine the current state using SMF knowledge data and sensor input. The Multi-Robot Task Planner generates behavior sequences to ensure that multiple robots can perform tasks without colliding. We validated the framework through experiments conducted in both virtual and real-world environments, achieving successful mission completion with an average error of approximately 0.117m.
ISSN:2642-3901
DOI:10.23919/ICCAS63016.2024.10773065