Semantic Knowledge-Based Mission Planning Method According to Robot Characteristics in Outdoor Environment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the field of robots, many studies have been proposed to introduce advanced cognitive science into robot systems based on existing numerical information. These research directions allow robots to utilize their knowledge to simplify, understand, and efficiently plan their tasks. In this paper, we propose a semantic knowledge-based robot mission planning method in an irregular three-dimensional environment. We introduce an advanced robot mission planning method that defines the knowledge of robots and driving places based on ontology and utilizes the defined semantic knowledge. To this end, we define a robot domain based on Planning Domain Definition Language (PDDL) and solve it through a classical planner to plan the mission. Finally, we validate the suitability of the proposed method by conducting experiments with real-world robots within the campus environment and comparing them with general PDDL-based methods.

Original languageEnglish
Title of host publicationIntelligent Autonomous Systems 18 - Volume 1 Proceedings of the 18th International Conference IAS18-2023
EditorsSoon-Geul Lee, Jinung An, Nak Young Chong, Marcus Strand, Joo H. Kim
PublisherSpringer Science and Business Media Deutschland GmbH
Pages367-377
Number of pages11
ISBN (Print)9783031448508
DOIs
StatePublished - 2024
Externally publishedYes
Event18th International Conference on Intelligent Autonomous Systems, IAS18 2023 - Suwon, Korea, Republic of
Duration: 4 Jul 20237 Jul 2023

Publication series

NameLecture Notes in Networks and Systems
Volume795
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference18th International Conference on Intelligent Autonomous Systems, IAS18 2023
Country/TerritoryKorea, Republic of
CitySuwon
Period4/07/237/07/23

Keywords

  • Planning domain definition language
  • Robot mission planning
  • Semantic knowledge

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