Citation:
Yi Yang,Yongjie Pang,Hongwei Li and Rubo Zhang.Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions[J].Journal of Marine Science and Application,2014,(3):333-339.[doi:10.1007/s11804-014-1265-7]
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Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions
Yi Yang,Yongjie Pang,Hongwei Li and Rubo Zhang.Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions[J].Journal of Marine Science and Application,2014,(3):333-339.[doi:10.1007/s11804-014-1265-7]
Info
- Title:
- Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions
- Author(s):
- Yi Yang; Yongjie Pang; Hongwei Li and Rubo Zhang
- Affilations:
- Keywords:
- self-propelled model; local path planning; Q learning; obstacle avoidance; reinforcement learning
- DOI:
- 10.1007/s11804-014-1265-7
- Abstract:
- Conducting hydrodynamic and physical motion simulation tests using a large-scale self-propelled model under actual wave conditions is an important means for researching environmental adaptability of ships. During the navigation test of the self-propelled model, the complex environment including various port facilities, navigation facilities, and the ships nearby must be considered carefully, because in this dense environment the impact of sea waves and winds on the model is particularly significant. In order to improve the security of the self-propelled model, this paper introduces the Q learning based on reinforcement learning combined with chaotic ideas for the model’s collision avoidance, in order to improve the reliability of the local path planning. Simulation and sea test results show that this algorithm is a better solution for collision avoidance of the self navigation model under the interference of sea winds and waves with good adaptability.
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Memo
- Memo:
- Supported by the National Natural Science Foundation of China under Grant No.61100005.
