A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning
This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/416 |
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| author | Ying Chen Zhe-Ming Lu Jia-Lin Cui Hao Luo Yang-Ming Zheng |
| author_facet | Ying Chen Zhe-Ming Lu Jia-Lin Cui Hao Luo Yang-Ming Zheng |
| author_sort | Ying Chen |
| collection | DOAJ |
| description | This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot’s adaptability and path optimization capabilities in dynamic environments. In particular, Re-DQN improves the stability of the training process through a dynamic incentive layer and achieves more comprehensive area coverage and shorter planning times in high-dimensional continuous state spaces. Simulation results show that Re-DQN outperforms the other algorithms in terms of performance, convergence speed, and stability, making it a robust solution for comprehensive coverage path planning. Future work will focus on testing and optimizing Re-DQN in more complex environments and exploring its application in multi-robot systems to enhance collaboration and communication. |
| format | Article |
| id | doaj-art-87a5cd36896e4380b02f3c9641ca5008 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-87a5cd36896e4380b02f3c9641ca50082025-01-24T13:48:50ZengMDPI AGSensors1424-82202025-01-0125241610.3390/s25020416A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement LearningYing Chen0Zhe-Ming Lu1Jia-Lin Cui2Hao Luo3Yang-Ming Zheng4Center for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, ChinaCenter for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, ChinaCenter for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, ChinaCenter for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, ChinaCenter for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, ChinaThis paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot’s adaptability and path optimization capabilities in dynamic environments. In particular, Re-DQN improves the stability of the training process through a dynamic incentive layer and achieves more comprehensive area coverage and shorter planning times in high-dimensional continuous state spaces. Simulation results show that Re-DQN outperforms the other algorithms in terms of performance, convergence speed, and stability, making it a robust solution for comprehensive coverage path planning. Future work will focus on testing and optimizing Re-DQN in more complex environments and exploring its application in multi-robot systems to enhance collaboration and communication.https://www.mdpi.com/1424-8220/25/2/416path planningcomplete coverage path planningreward functioncuriosity-driven explorationdynamic ε-greedy strategydynamic environments |
| spellingShingle | Ying Chen Zhe-Ming Lu Jia-Lin Cui Hao Luo Yang-Ming Zheng A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning Sensors path planning complete coverage path planning reward function curiosity-driven exploration dynamic ε-greedy strategy dynamic environments |
| title | A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning |
| title_full | A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning |
| title_fullStr | A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning |
| title_full_unstemmed | A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning |
| title_short | A Complete Coverage Path Planning Algorithm for Lawn Mowing Robots Based on Deep Reinforcement Learning |
| title_sort | complete coverage path planning algorithm for lawn mowing robots based on deep reinforcement learning |
| topic | path planning complete coverage path planning reward function curiosity-driven exploration dynamic ε-greedy strategy dynamic environments |
| url | https://www.mdpi.com/1424-8220/25/2/416 |
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