Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot
Reconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dy...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9131750/ |
| _version_ | 1818932196864425984 |
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| author | Ku Ping Cheng Rajesh Elara Mohan Nguyen Huu Khanh Nhan Anh Vu Le |
| author_facet | Ku Ping Cheng Rajesh Elara Mohan Nguyen Huu Khanh Nhan Anh Vu Le |
| author_sort | Ku Ping Cheng |
| collection | DOAJ |
| description | Reconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dynamic obstacles. However, the automation of reconfigurable robots is more challenging than fix-shaped robots due to the increased possible combinations of robot actions and the navigation difficulty in obstacle-rich environments. This paper develops a systematic strategy to construct a model of hinged-Tetromino (hTetro) reconfigurable robot in the workspace and proposes a genetic algorithm-based method (hTetro-GA) to achieve path planning for hTetro robots. The proposed algorithm considers hTetro path planning as a multi-objective optimization problem and evaluates the performance of the outcome based on four customized fitness objective functions. In this work, the proposed hTetro-GA is tested in six virtual environments with various obstacle layouts and characteristics and with different population sizes. The algorithm generates Pareto-optimal solutions that achieve desire robot configurations in these settings, with O-shaped and I-shaped morphologies being the more efficient configurations selected from the genetic algorithm. The proposed algorithm is implemented and tested on real hTetro platform, and the framework of this work could be adopted to other robot platforms with multiple configurations to perform multi-objective based path planning. |
| first_indexed | 2024-12-20T04:28:39Z |
| format | Article |
| id | doaj.art-e9cd38b4bb934778bad948bbee8132dc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:28:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e9cd38b4bb934778bad948bbee8132dc2022-12-21T19:53:27ZengIEEEIEEE Access2169-35362020-01-01812126712128410.1109/ACCESS.2020.30065799131750Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling RobotKu Ping Cheng0https://orcid.org/0000-0002-1285-6143Rajesh Elara Mohan1https://orcid.org/0000-0001-6504-1530Nguyen Huu Khanh Nhan2https://orcid.org/0000-0002-7347-7446Anh Vu Le3https://orcid.org/0000-0002-4804-7540ROAR Laboratory, Engineering Product Development Pillar, Singapore University of Technology and Design, SingaporeROAR Laboratory, Engineering Product Development Pillar, Singapore University of Technology and Design, SingaporeOptoelectronics Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, VietnamROAR Laboratory, Engineering Product Development Pillar, Singapore University of Technology and Design, SingaporeReconfigurable robots have received broad research interest due to the high dexterity they provide and the complex actions they could perform. Robots with reconfigurability are perfect candidates in tasks like exploration or rescue missions in environments with complicated obstacle layout or with dynamic obstacles. However, the automation of reconfigurable robots is more challenging than fix-shaped robots due to the increased possible combinations of robot actions and the navigation difficulty in obstacle-rich environments. This paper develops a systematic strategy to construct a model of hinged-Tetromino (hTetro) reconfigurable robot in the workspace and proposes a genetic algorithm-based method (hTetro-GA) to achieve path planning for hTetro robots. The proposed algorithm considers hTetro path planning as a multi-objective optimization problem and evaluates the performance of the outcome based on four customized fitness objective functions. In this work, the proposed hTetro-GA is tested in six virtual environments with various obstacle layouts and characteristics and with different population sizes. The algorithm generates Pareto-optimal solutions that achieve desire robot configurations in these settings, with O-shaped and I-shaped morphologies being the more efficient configurations selected from the genetic algorithm. The proposed algorithm is implemented and tested on real hTetro platform, and the framework of this work could be adopted to other robot platforms with multiple configurations to perform multi-objective based path planning.https://ieeexplore.ieee.org/document/9131750/Reconfigurable robottiling roboticsmulti-objective path planninggenetic algorithmNSGA-II |
| spellingShingle | Ku Ping Cheng Rajesh Elara Mohan Nguyen Huu Khanh Nhan Anh Vu Le Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot IEEE Access Reconfigurable robot tiling robotics multi-objective path planning genetic algorithm NSGA-II |
| title | Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot |
| title_full | Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot |
| title_fullStr | Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot |
| title_full_unstemmed | Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot |
| title_short | Multi-Objective Genetic Algorithm-Based Autonomous Path Planning for Hinged-Tetro Reconfigurable Tiling Robot |
| title_sort | multi objective genetic algorithm based autonomous path planning for hinged tetro reconfigurable tiling robot |
| topic | Reconfigurable robot tiling robotics multi-objective path planning genetic algorithm NSGA-II |
| url | https://ieeexplore.ieee.org/document/9131750/ |
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