Research on AGV Path Planning Based on Improved DQN Algorithm
Traditional deep reinforcement learning methods suffer from slow convergence speeds and poor adaptability in complex environments and are prone to falling into local optima in AGV system applications. To address these issues, in this paper, an adaptive path planning algorithm with an improved Deep Q...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4685 |
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| author | Qian Xiao Tengteng Pan Kexin Wang Shuoming Cui |
| author_facet | Qian Xiao Tengteng Pan Kexin Wang Shuoming Cui |
| author_sort | Qian Xiao |
| collection | DOAJ |
| description | Traditional deep reinforcement learning methods suffer from slow convergence speeds and poor adaptability in complex environments and are prone to falling into local optima in AGV system applications. To address these issues, in this paper, an adaptive path planning algorithm with an improved Deep Q Network algorithm called the B-PER DQN algorithm is proposed. Firstly, a dynamic temperature adjustment mechanism is constructed, and the temperature parameters in the Boltzmann strategy are adaptively adjusted by analyzing the change trend of the recent reward window. Next, the Priority experience replay mechanism is introduced to improve the training efficiency and task diversity through experience grading sampling and random obstacle configuration. Then, a refined multi-objective reward function is designed, combined with direction guidance, step punishment, and end point reward, to effectively guide the agent in learning an efficient path. Our experimental results show that, compared with other algorithms, the improved algorithm proposed in this paper achieves a higher success rate and faster convergence in the same environment and represents an efficient and adaptive solution for reinforcement learning for path planning in complex environments. |
| format | Article |
| id | doaj-art-3243a3d7e2ab467aa3d85ea491d3c92c |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3243a3d7e2ab467aa3d85ea491d3c92c2025-08-20T03:02:58ZengMDPI AGSensors1424-82202025-07-012515468510.3390/s25154685Research on AGV Path Planning Based on Improved DQN AlgorithmQian Xiao0Tengteng Pan1Kexin Wang2Shuoming Cui3School of Intelligent Science Information Engineering, Shenyang University, Shenyang 110044, ChinaSchool of Intelligent Science Information Engineering, Shenyang University, Shenyang 110044, ChinaSchool of Intelligent Science Information Engineering, Shenyang University, Shenyang 110044, ChinaSchool of Intelligent Science Information Engineering, Shenyang University, Shenyang 110044, ChinaTraditional deep reinforcement learning methods suffer from slow convergence speeds and poor adaptability in complex environments and are prone to falling into local optima in AGV system applications. To address these issues, in this paper, an adaptive path planning algorithm with an improved Deep Q Network algorithm called the B-PER DQN algorithm is proposed. Firstly, a dynamic temperature adjustment mechanism is constructed, and the temperature parameters in the Boltzmann strategy are adaptively adjusted by analyzing the change trend of the recent reward window. Next, the Priority experience replay mechanism is introduced to improve the training efficiency and task diversity through experience grading sampling and random obstacle configuration. Then, a refined multi-objective reward function is designed, combined with direction guidance, step punishment, and end point reward, to effectively guide the agent in learning an efficient path. Our experimental results show that, compared with other algorithms, the improved algorithm proposed in this paper achieves a higher success rate and faster convergence in the same environment and represents an efficient and adaptive solution for reinforcement learning for path planning in complex environments.https://www.mdpi.com/1424-8220/25/15/4685automatic guided vehiclepath planningdeep Q networkdeep reinforcement learning |
| spellingShingle | Qian Xiao Tengteng Pan Kexin Wang Shuoming Cui Research on AGV Path Planning Based on Improved DQN Algorithm Sensors automatic guided vehicle path planning deep Q network deep reinforcement learning |
| title | Research on AGV Path Planning Based on Improved DQN Algorithm |
| title_full | Research on AGV Path Planning Based on Improved DQN Algorithm |
| title_fullStr | Research on AGV Path Planning Based on Improved DQN Algorithm |
| title_full_unstemmed | Research on AGV Path Planning Based on Improved DQN Algorithm |
| title_short | Research on AGV Path Planning Based on Improved DQN Algorithm |
| title_sort | research on agv path planning based on improved dqn algorithm |
| topic | automatic guided vehicle path planning deep Q network deep reinforcement learning |
| url | https://www.mdpi.com/1424-8220/25/15/4685 |
| work_keys_str_mv | AT qianxiao researchonagvpathplanningbasedonimproveddqnalgorithm AT tengtengpan researchonagvpathplanningbasedonimproveddqnalgorithm AT kexinwang researchonagvpathplanningbasedonimproveddqnalgorithm AT shuomingcui researchonagvpathplanningbasedonimproveddqnalgorithm |