Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915012511006720 |
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| author | Li, Keqin Liu, Lipeng Chen, Jiajing Yu, Dezhi Zhou, Xiaofan Li, Ming Wang, Congyu Li, Zhao |
| author_facet | Li, Keqin Liu, Lipeng Chen, Jiajing Yu, Dezhi Zhou, Xiaofan Li, Ming Wang, Congyu Li, Zhao |
| contents | In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-time decision making through PPO, and uses Dijkstra algorithm to plan the global optimal path, thus ensuring high navigation accuracy and significantly improving the efficiency of path planning. Specifically, PPO enables robots to quickly adapt and optimize action strategies in dynamic environments through its stable policy updating mechanism. Dijkstra's algorithm ensures global optimal path planning in static environment. Finally, through the comparison experiment and analysis of the proposed framework with the traditional algorithm, the results show that the PP-D method has significant advantages in improving the accuracy of navigation prediction and enhancing the robustness of the system. Especially in complex warehouse layout, PP-D method can find the optimal path more accurately and reduce collision and stagnation. This proves the reliability and effectiveness of the robot in the study of complex warehouse layout navigation algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout Li, Keqin Liu, Lipeng Chen, Jiajing Yu, Dezhi Zhou, Xiaofan Li, Ming Wang, Congyu Li, Zhao Robotics Artificial Intelligence In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-time decision making through PPO, and uses Dijkstra algorithm to plan the global optimal path, thus ensuring high navigation accuracy and significantly improving the efficiency of path planning. Specifically, PPO enables robots to quickly adapt and optimize action strategies in dynamic environments through its stable policy updating mechanism. Dijkstra's algorithm ensures global optimal path planning in static environment. Finally, through the comparison experiment and analysis of the proposed framework with the traditional algorithm, the results show that the PP-D method has significant advantages in improving the accuracy of navigation prediction and enhancing the robustness of the system. Especially in complex warehouse layout, PP-D method can find the optimal path more accurately and reduce collision and stagnation. This proves the reliability and effectiveness of the robot in the study of complex warehouse layout navigation algorithm. |
| title | Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06128 |