Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer
Research on trajectory generation algorithms for unmanned ground vehicles (UGVs) has been actively conducted due to the rapid increase in their use across various fields. Trajectory generation for UGVs requires a high level of precision, as various parameters determine the trajectory’s ef...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10705302/ |
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| author | Chang-Hun Ji Gyeonghun Lim Youn-Hee Han Sungtae Moon |
| author_facet | Chang-Hun Ji Gyeonghun Lim Youn-Hee Han Sungtae Moon |
| author_sort | Chang-Hun Ji |
| collection | DOAJ |
| description | Research on trajectory generation algorithms for unmanned ground vehicles (UGVs) has been actively conducted due to the rapid increase in their use across various fields. Trajectory generation for UGVs requires a high level of precision, as various parameters determine the trajectory’s efficiency and safety. Notably, maximum velocity and acceleration are critical factors impacting trajectory performance. In this paper, we propose a novel algorithm that utilizes reinforcement learning to determine the optimal maximum velocity and acceleration in real-time within dynamic environments. The proposed algorithm overcomes the limitations of traditional fixed parameter settings by determining parameters through real-time environmental adaptation. Furthermore, we also propose a PX4-ROS2 based reinforcement learning framework for achieving stable zero-shot sim-to-real transfer. Experimental results in simulation and real-world environments show that the proposed method significantly improves trajectory safety and efficiency while also demonstrating excellent adaptability to changing environments. Furthermore, validation through identical experimental results in both simulation and real-world environments confirms a stable zero-shot sim-to-real transfer. |
| format | Article |
| id | doaj-art-a19ecba8a66f4ce4a730d06bb9ba5d60 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a19ecba8a66f4ce4a730d06bb9ba5d602024-11-23T00:01:45ZengIEEEIEEE Access2169-35362024-01-011217166217167410.1109/ACCESS.2024.347393510705302Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real TransferChang-Hun Ji0https://orcid.org/0009-0003-4372-8675Gyeonghun Lim1Youn-Hee Han2https://orcid.org/0000-0002-5835-7972Sungtae Moon3Department of Future Convergence Engineering, Korea University of Technology and Education, Cheonan-si, South KoreaDepartment of Intelligent System and Robotics, Chungbuk National University, Cheongju-si, South KoreaDepartment of Future Convergence Engineering, Korea University of Technology and Education, Cheonan-si, South KoreaDepartment of Intelligent System and Robotics, Chungbuk National University, Cheongju-si, South KoreaResearch on trajectory generation algorithms for unmanned ground vehicles (UGVs) has been actively conducted due to the rapid increase in their use across various fields. Trajectory generation for UGVs requires a high level of precision, as various parameters determine the trajectory’s efficiency and safety. Notably, maximum velocity and acceleration are critical factors impacting trajectory performance. In this paper, we propose a novel algorithm that utilizes reinforcement learning to determine the optimal maximum velocity and acceleration in real-time within dynamic environments. The proposed algorithm overcomes the limitations of traditional fixed parameter settings by determining parameters through real-time environmental adaptation. Furthermore, we also propose a PX4-ROS2 based reinforcement learning framework for achieving stable zero-shot sim-to-real transfer. Experimental results in simulation and real-world environments show that the proposed method significantly improves trajectory safety and efficiency while also demonstrating excellent adaptability to changing environments. Furthermore, validation through identical experimental results in both simulation and real-world environments confirms a stable zero-shot sim-to-real transfer.https://ieeexplore.ieee.org/document/10705302/Trajectory generationreinforcement learningreal-time parameter optimizationsim-to-real |
| spellingShingle | Chang-Hun Ji Gyeonghun Lim Youn-Hee Han Sungtae Moon Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer IEEE Access Trajectory generation reinforcement learning real-time parameter optimization sim-to-real |
| title | Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer |
| title_full | Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer |
| title_fullStr | Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer |
| title_full_unstemmed | Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer |
| title_short | Real-Time Parameter Control for Trajectory Generation Using Reinforcement Learning With Zero-Shot Sim-to-Real Transfer |
| title_sort | real time parameter control for trajectory generation using reinforcement learning with zero shot sim to real transfer |
| topic | Trajectory generation reinforcement learning real-time parameter optimization sim-to-real |
| url | https://ieeexplore.ieee.org/document/10705302/ |
| work_keys_str_mv | AT changhunji realtimeparametercontrolfortrajectorygenerationusingreinforcementlearningwithzeroshotsimtorealtransfer AT gyeonghunlim realtimeparametercontrolfortrajectorygenerationusingreinforcementlearningwithzeroshotsimtorealtransfer AT younheehan realtimeparametercontrolfortrajectorygenerationusingreinforcementlearningwithzeroshotsimtorealtransfer AT sungtaemoon realtimeparametercontrolfortrajectorygenerationusingreinforcementlearningwithzeroshotsimtorealtransfer |