Ontology-based task planning for autonomous unmanned system: framework and principle

Pang, Weijian; Gu, Wenguan; Li, Hui

doi:10.1088/1742-6596/2253/1/012018

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The amount of available modeling elements, such as skills and primitives, is managed through an ontology where domain knowledge such as tasks, skills, and primitives are defined. The characteristics of ontology ease the sharing of knowledge, provide clearly structured data, and enable accurate reasoning of the data [60]. Therefore, we also adopted the benefits of ontology for knowledge sharing among heterogeneous systems and human-machine interactions.…”

Section: The Implementationmentioning

confidence: 99%

RTMN 2.0—An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration

Zhang Sprenger,

Corrales Ramón,

Baier

2023

Applied Sciences

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This paper describes RTMN 2.0, an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. Intuitive robot programming allows those without programming expertise to plan and control robots through easily understandable predefined modeling notations. These notations achieve no-code programming and serve as templates for users to create their processes via drag-and-drop functions with graphical representations. The design of the graphical user interface is based on a user survey and gaps identified in the literature We validate our survey through the most influential technology acceptance models, with two major factors: the perceived ease of use and perceived usefulness. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modeling language, RTMN 2.0 concentrates on human–robot collaboration (HRC), which represents the current trend of the industry shift from “mass-production” to “mass-customization”. The biggest contribution that RTMN 2.0 makes is the creation of synergy between HRC modes (based on ISO standards) and HRC task types in the literature. They are modeled as five different HRC task notations: Coexistence Fence, Sequential Cooperation SMS, Teaching HG, Parallel Cooperation SSM, and Collaboration PFL. Both collaboration and safety criteria are defined for each notation. While traditional isolated robot systems in “mass-production” environments provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity in order to be adapted to the variability of customized products. Therefore, human–robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in the new “mass-customization” industrial environments. HRC has made a great impact on the robotic industry: it leads to increased efficiency, reduced costs, and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension of RTMN 2.0 includes the following notations: HRC tasks, requirements, Key Performance Indicators (KPIs), condition checks and decision making, join/split, and data association. With these additional elements, RTMN 2.0 meets the full range of criteria for agile manufacturing—light-out manufacturing is a manufacturing philosophy that does not rely on human labor.

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Section: The Implementationmentioning

confidence: 99%

RTMN 2.0—An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration

Zhang Sprenger,

Corrales Ramón,

Baier

2023

Applied Sciences

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Robotic Process Automation with Ontology-enabled Skill-based Robot Task Model and Notation (RTMN)

Sprenger

Ribeaud

2022

2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)

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RTMN 2.0 – an Extension of Robot Task Modelling and Notation (RTMN) Focus on Human Robot Collaboration

Zhang Sprenger,

Corrales Ramón,

Baier

2023

Preprint

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This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

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Ontology-based task planning for autonomous unmanned system: framework and principle

Cited by 3 publications

References 21 publications

RTMN 2.0—An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration

RTMN 2.0—An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration

Robotic Process Automation with Ontology-enabled Skill-based Robot Task Model and Notation (RTMN)

RTMN 2.0 – an Extension of Robot Task Modelling and Notation (RTMN) Focus on Human Robot Collaboration

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