Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
The purpose of this article is to investigate a suitable path planning algorithm for a multi-agent-based Plug & Produce system that can run online during manufacturing. This is needed since in such systems, resources can move around frequently, making it hard to manually create robot paths. To f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/22/12114 |
| _version_ | 1797460341273133056 |
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| author | Sudha Ramasamy Kristina M. Eriksson Fredrik Danielsson Mikael Ericsson |
| author_facet | Sudha Ramasamy Kristina M. Eriksson Fredrik Danielsson Mikael Ericsson |
| author_sort | Sudha Ramasamy |
| collection | DOAJ |
| description | The purpose of this article is to investigate a suitable path planning algorithm for a multi-agent-based Plug & Produce system that can run online during manufacturing. This is needed since in such systems, resources can move around frequently, making it hard to manually create robot paths. To find a suitable algorithm and verify that it can be used online in a Plug & Produce system, a comparative study between various existing sampling-based path planning algorithms was conducted. Much research exists on path planning carried out offline; however, not so much is performed in online path planning. The specific requirements for Plug & Produce are to generate a path fast enough to eliminate manufacturing delays, to make the path energy efficient, and that it run fast enough to complete the task. The paths are generated in a simulation environment and the generated paths are tested for robot configuration errors and errors due to the target being out of reach. The error-free generated paths are then tested on an industrial test bed environment, and the energy consumed by each path was measured and validated with an energy meter. The results show that all the implemented optimal sampling-based algorithms can be used for some scenarios, but that adaptive RRT and adaptive RRT* are more suitable for online applications in multi-agent systems (MAS) due to a faster generation of paths, even though the environment has more constraints. For each generated path the computational time of the algorithm, move-along time and energy consumed are measured, evaluated, compared, and presented in the article. |
| first_indexed | 2024-03-09T17:04:40Z |
| format | Article |
| id | doaj.art-940941cd488e4dc1920127bdd705bf1a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:04:40Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-940941cd488e4dc1920127bdd705bf1a2023-11-24T14:26:15ZengMDPI AGApplied Sciences2076-34172023-11-0113221211410.3390/app132212114Sampling-Based Path Planning Algorithm for a Plug & Produce EnvironmentSudha Ramasamy0Kristina M. Eriksson1Fredrik Danielsson2Mikael Ericsson3Department of Engineering Science, University West, 461 86 Trollhättan, SwedenDepartment of Engineering Science, University West, 461 86 Trollhättan, SwedenDepartment of Engineering Science, University West, 461 86 Trollhättan, SwedenDepartment of Engineering Science, University West, 461 86 Trollhättan, SwedenThe purpose of this article is to investigate a suitable path planning algorithm for a multi-agent-based Plug & Produce system that can run online during manufacturing. This is needed since in such systems, resources can move around frequently, making it hard to manually create robot paths. To find a suitable algorithm and verify that it can be used online in a Plug & Produce system, a comparative study between various existing sampling-based path planning algorithms was conducted. Much research exists on path planning carried out offline; however, not so much is performed in online path planning. The specific requirements for Plug & Produce are to generate a path fast enough to eliminate manufacturing delays, to make the path energy efficient, and that it run fast enough to complete the task. The paths are generated in a simulation environment and the generated paths are tested for robot configuration errors and errors due to the target being out of reach. The error-free generated paths are then tested on an industrial test bed environment, and the energy consumed by each path was measured and validated with an energy meter. The results show that all the implemented optimal sampling-based algorithms can be used for some scenarios, but that adaptive RRT and adaptive RRT* are more suitable for online applications in multi-agent systems (MAS) due to a faster generation of paths, even though the environment has more constraints. For each generated path the computational time of the algorithm, move-along time and energy consumed are measured, evaluated, compared, and presented in the article.https://www.mdpi.com/2076-3417/13/22/12114adaptive RRT*path planningPlug & ProducePRMRRT*sampling-based algorithms |
| spellingShingle | Sudha Ramasamy Kristina M. Eriksson Fredrik Danielsson Mikael Ericsson Sampling-Based Path Planning Algorithm for a Plug & Produce Environment Applied Sciences adaptive RRT* path planning Plug & Produce PRM RRT* sampling-based algorithms |
| title | Sampling-Based Path Planning Algorithm for a Plug & Produce Environment |
| title_full | Sampling-Based Path Planning Algorithm for a Plug & Produce Environment |
| title_fullStr | Sampling-Based Path Planning Algorithm for a Plug & Produce Environment |
| title_full_unstemmed | Sampling-Based Path Planning Algorithm for a Plug & Produce Environment |
| title_short | Sampling-Based Path Planning Algorithm for a Plug & Produce Environment |
| title_sort | sampling based path planning algorithm for a plug produce environment |
| topic | adaptive RRT* path planning Plug & Produce PRM RRT* sampling-based algorithms |
| url | https://www.mdpi.com/2076-3417/13/22/12114 |
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