Knowledge-Based Planning for Human-Robot Collaborative Tasks
Human-robot collaboration is a promising alternative to full automation and manual labour. Collaborative robots are considered safe and individual robot actions can often be easily programmed, for example, by physical hand-guiding. Coordinated collaboration, where tasks and the environment are share...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11052273/ |
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| _version_ | 1849423269678022656 |
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| author | Alexandre Angleraud Metodi Netzev Roel Pieters |
| author_facet | Alexandre Angleraud Metodi Netzev Roel Pieters |
| author_sort | Alexandre Angleraud |
| collection | DOAJ |
| description | Human-robot collaboration is a promising alternative to full automation and manual labour. Collaborative robots are considered safe and individual robot actions can often be easily programmed, for example, by physical hand-guiding. Coordinated collaboration, where tasks and the environment are shared, cannot be so easily achieved, due to continuously changing conditions and actions that need to be triggered at unknown instances. Besides, knowledge required for collaboration is difficult to program into robotic systems. This paper presents a system architecture that aims at facilitating human-robot collaboration by alleviating the need for pre-programmed information and exploring ways to teach new skills. This is done by reducing the amount of information required to program tasks by utilizing a knowledge base that represents knowledge on tasks, actions and the world. Automatic reasoning over conditions and properties of the knowledge is then utilized to generate available actions and action plans in order to complete the shared tasks. Moreover, learning new tasks is enabled by extending the original knowledge base, concatenating available actions and tasks into news bricks of knowledge. Two examples, a kitting task and a handover task, serve to validate the system architecture and exemplify its usage. Experiments demonstrate that by combining reasoning methods and knowledge-based planning, high-level shared tasks can be generated and executed, and robots can act reliable as teammate to human operators. |
| format | Article |
| id | doaj-art-c0329b3aaaeb4bbbbb8e198db852225e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c0329b3aaaeb4bbbbb8e198db852225e2025-08-20T03:30:40ZengIEEEIEEE Access2169-35362025-01-011311075211076410.1109/ACCESS.2025.358346911052273Knowledge-Based Planning for Human-Robot Collaborative TasksAlexandre Angleraud0https://orcid.org/0000-0002-2291-1329Metodi Netzev1Roel Pieters2https://orcid.org/0000-0001-6728-304XUnit of Automation Technology and Mechanical Engineering, Tampere University, Tampere, FinlandUnit of Automation Technology and Mechanical Engineering, Tampere University, Tampere, FinlandUnit of Automation Technology and Mechanical Engineering, Tampere University, Tampere, FinlandHuman-robot collaboration is a promising alternative to full automation and manual labour. Collaborative robots are considered safe and individual robot actions can often be easily programmed, for example, by physical hand-guiding. Coordinated collaboration, where tasks and the environment are shared, cannot be so easily achieved, due to continuously changing conditions and actions that need to be triggered at unknown instances. Besides, knowledge required for collaboration is difficult to program into robotic systems. This paper presents a system architecture that aims at facilitating human-robot collaboration by alleviating the need for pre-programmed information and exploring ways to teach new skills. This is done by reducing the amount of information required to program tasks by utilizing a knowledge base that represents knowledge on tasks, actions and the world. Automatic reasoning over conditions and properties of the knowledge is then utilized to generate available actions and action plans in order to complete the shared tasks. Moreover, learning new tasks is enabled by extending the original knowledge base, concatenating available actions and tasks into news bricks of knowledge. Two examples, a kitting task and a handover task, serve to validate the system architecture and exemplify its usage. Experiments demonstrate that by combining reasoning methods and knowledge-based planning, high-level shared tasks can be generated and executed, and robots can act reliable as teammate to human operators.https://ieeexplore.ieee.org/document/11052273/Human-robot collaborationknowledge-based systemsrobot programming |
| spellingShingle | Alexandre Angleraud Metodi Netzev Roel Pieters Knowledge-Based Planning for Human-Robot Collaborative Tasks IEEE Access Human-robot collaboration knowledge-based systems robot programming |
| title | Knowledge-Based Planning for Human-Robot Collaborative Tasks |
| title_full | Knowledge-Based Planning for Human-Robot Collaborative Tasks |
| title_fullStr | Knowledge-Based Planning for Human-Robot Collaborative Tasks |
| title_full_unstemmed | Knowledge-Based Planning for Human-Robot Collaborative Tasks |
| title_short | Knowledge-Based Planning for Human-Robot Collaborative Tasks |
| title_sort | knowledge based planning for human robot collaborative tasks |
| topic | Human-robot collaboration knowledge-based systems robot programming |
| url | https://ieeexplore.ieee.org/document/11052273/ |
| work_keys_str_mv | AT alexandreangleraud knowledgebasedplanningforhumanrobotcollaborativetasks AT metodinetzev knowledgebasedplanningforhumanrobotcollaborativetasks AT roelpieters knowledgebasedplanningforhumanrobotcollaborativetasks |