Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches
Motion planning is a fundamental challenge in robotics, involving the creation of trajectories from start to goal states while meeting constraints like collision avoidance and joint limits. Its complexity increases with the number of robot joints. Several traditional approaches tackle this problem,...
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| Format: | Article |
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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/10930422/ |
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| author | Gabriel Peixoto de Carvalho Tetsuya Sawanobori Takato Horii |
| author_facet | Gabriel Peixoto de Carvalho Tetsuya Sawanobori Takato Horii |
| author_sort | Gabriel Peixoto de Carvalho |
| collection | DOAJ |
| description | Motion planning is a fundamental challenge in robotics, involving the creation of trajectories from start to goal states while meeting constraints like collision avoidance and joint limits. Its complexity increases with the number of robot joints. Several traditional approaches tackle this problem, such as sampling motion planning, grid-based methods, potential fields, and optimization techniques. Recent advancements in deep neural networks, reinforcement learning, and large language models enable new possibilities for solving motion planning problems by improving sampling efficiency, optimizing control policies, and enabling task planning through natural language prompts. This survey comprehensively reviews these novel approaches, providing background knowledge, analyzing key contributions, and identifying common patterns, limitations, and research gaps. Our work is the first to integrate all three major data-driven approaches, discussing their applications and future research directions. |
| format | Article |
| id | doaj-art-201dbcbc0b2b4ebb89db743c69ec5e18 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-201dbcbc0b2b4ebb89db743c69ec5e182025-08-20T02:54:22ZengIEEEIEEE Access2169-35362025-01-0113521955224510.1109/ACCESS.2025.355222510930422Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model ApproachesGabriel Peixoto de Carvalho0https://orcid.org/0000-0001-8770-573XTetsuya Sawanobori1Takato Horii2https://orcid.org/0000-0001-5374-0887Graduate School of Engineering Science, Osaka University, Osaka, JapanConnected Robotics Inc., Tokyo University of Agriculture and Technology Koganei Campus, Tokyo, JapanGraduate School of Engineering Science, Osaka University, Osaka, JapanMotion planning is a fundamental challenge in robotics, involving the creation of trajectories from start to goal states while meeting constraints like collision avoidance and joint limits. Its complexity increases with the number of robot joints. Several traditional approaches tackle this problem, such as sampling motion planning, grid-based methods, potential fields, and optimization techniques. Recent advancements in deep neural networks, reinforcement learning, and large language models enable new possibilities for solving motion planning problems by improving sampling efficiency, optimizing control policies, and enabling task planning through natural language prompts. This survey comprehensively reviews these novel approaches, providing background knowledge, analyzing key contributions, and identifying common patterns, limitations, and research gaps. Our work is the first to integrate all three major data-driven approaches, discussing their applications and future research directions.https://ieeexplore.ieee.org/document/10930422/Roboticsdeep learningsurveylarge language modelsneural networksreinforcement learning |
| spellingShingle | Gabriel Peixoto de Carvalho Tetsuya Sawanobori Takato Horii Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches IEEE Access Robotics deep learning survey large language models neural networks reinforcement learning |
| title | Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches |
| title_full | Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches |
| title_fullStr | Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches |
| title_full_unstemmed | Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches |
| title_short | Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches |
| title_sort | data driven motion planning a survey on deep neural networks reinforcement learning and large language model approaches |
| topic | Robotics deep learning survey large language models neural networks reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10930422/ |
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