Path Planning of Indoor Mobile Educational Robot Based on Improved Deep Reinforcement Learning

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dc.contributor.authorWeiping Zhu
dc.contributor.authorWonchana Katsri
dc.contributor.correspondenceW. Zhu; School of Humanities and Education, Nanchang Institute of Technology, Nanchang, Jiangxi, China; email: zhuweiping1991410@163.com
dc.date.accessioned2025-03-10T07:34:44Z
dc.date.available2025-03-10T07:34:44Z
dc.date.issued2023
dc.description.abstractWith the maturity of artificial intelligence and Internet of Things technology, the research on robots has also become one of the hotspots of artificial intelligence. Indoor mobile educational robots are an important part of machine intelligence. Research on the path of indoor mobile educational robots has become a key point in machine research. The purpose of this paper is to study the path planning of indoor mobile educational robots to improve deep reinforcement learning. This article first summarizes the research status of mobile educational robots at home and abroad. On this basis, the kinematics model of the indoor mobile educational robot is researched and analyzed. This article systematically elaborates the path planning based on the Actor-Critic algorithm and the deep reinforcement learning training model based on the minimum depth of field information. And use comparative analysis method, observation method and other research methods to carry out experimental research on the theme of this article. Research shows that the Actor-Critic algorithm proposed in this paper is shorter in path planning time and path distance than traditional algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.identifier.citationLecture Notes on Data Engineering and Communications Technologies
dc.identifier.doi10.1007/978-981-19-3632-6_24
dc.identifier.issn23674512
dc.identifier.scopus2-s2.0-85133707212
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4545
dc.languageEnglish
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights.holderScopus
dc.subjectApplied research
dc.subjectDeep learning
dc.subjectMobile robots
dc.subjectPath planning
dc.titlePath Planning of Indoor Mobile Educational Robot Based on Improved Deep Reinforcement Learning
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85133707212&doi=10.1007%2f978-981-19-3632-6_24&partnerID=40&md5=d366e85b6c2090d62532b8e838de2301
oaire.citation.endPage195
oaire.citation.startPage187
oaire.citation.volume122
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