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RoboPlanner: autonomous robotic action planning via knowledge graph queries

Authors:

Ajay Kattepur,

Balamuralidhar PAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 953 - 956

https://doi.org/10.1145/3297280.3297568

Published: 08 April 2019 Publication History

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Abstract

Autonomous robots are being increasingly integrated into Industry 4.0 manufacturing and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex tasks, the autonomous robots require robust action plans, that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases, that may be queried during planning and execution. In this paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we model the knowledge of world models, robotic capabilities and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning, that can adapt at runtime. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses.

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Cited By

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Wan YLiu YChen ZChen CLi XHu FPackianather M(2024)Making knowledge graphs work for smart manufacturing: Research topics, applications and prospectsJournal of Manufacturing Systems10.1016/j.jmsy.2024.07.00976(103-132)Online publication date: Oct-2024
https://doi.org/10.1016/j.jmsy.2024.07.009
Tubis ARohman J(2023)Intelligent Warehouse in Industry 4.0—Systematic Literature ReviewSensors10.3390/s2308410523:8(4105)Online publication date: 19-Apr-2023
https://doi.org/10.3390/s23084105
Wu RFan CHou HZhang ZWang LZhao XChao F(2023)An Action Evaluation and Scaling Algorithm for Robot Motion PlanningIntelligent Robotics and Applications10.1007/978-981-99-6492-5_34(392-403)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-6492-5_34
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Index Terms

RoboPlanner: autonomous robotic action planning via knowledge graph queries
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic autonomy
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Hierarchical data models

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Published In

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 08 April 2019

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

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Wan YLiu YChen ZChen CLi XHu FPackianather M(2024)Making knowledge graphs work for smart manufacturing: Research topics, applications and prospectsJournal of Manufacturing Systems10.1016/j.jmsy.2024.07.00976(103-132)Online publication date: Oct-2024
https://doi.org/10.1016/j.jmsy.2024.07.009
Tubis ARohman J(2023)Intelligent Warehouse in Industry 4.0—Systematic Literature ReviewSensors10.3390/s2308410523:8(4105)Online publication date: 19-Apr-2023
https://doi.org/10.3390/s23084105
Wu RFan CHou HZhang ZWang LZhao XChao F(2023)An Action Evaluation and Scaling Algorithm for Robot Motion PlanningIntelligent Robotics and Applications10.1007/978-981-99-6492-5_34(392-403)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-6492-5_34
Wang HPeng GWang HPeng G(2023)The Merging of Knowledge Management and New Information TechnologiesCollaborative Knowledge Management Through Product Lifecycle10.1007/978-981-19-9626-9_6(229-283)Online publication date: 1-Mar-2023
https://doi.org/10.1007/978-981-19-9626-9_6
Martinez-Gil JHoch TPichler MHeinzl BMoser BKurniawan KKiesling EKrause F(2023)Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive ReviewArtificial Intelligence in Manufacturing10.1007/978-3-031-46452-2_4(55-70)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-3-031-46452-2_4
Zhang SSun X(2022)Designing Social Interactions for Learning Personalized Knowledge in Service RobotsHuman-Computer Interaction. Technological Innovation10.1007/978-3-031-05409-9_47(656-671)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-05409-9_47
Kattepur A(2021)Knowledge-Driven Autonomous Robotic Action Planning for Industry 4.0Innovations in the Industrial Internet of Things (IIoT) and Smart Factory10.4018/978-1-7998-3375-8.ch011(149-171)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3375-8.ch011
Zhang SWang ZChen CDai YYe LSun XKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service RobotsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445767(1-12)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445767

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RoboPlanner: a pragmatic task planning framework for autonomous robots

Design and Simulation of Autonomous Mobile Robots Obstacle Avoidance System

Path tracing for ground wheeled robot in partially known environment

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RoboPlanner: a pragmatic task planning framework for autonomous robots

Design and Simulation of Autonomous Mobile Robots Obstacle Avoidance System

Path tracing for ground wheeled robot in partially known environment

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