Improved reinforcement learning for collision-free local path planning of dynamic obstacle

The rapid development of artificial intelligence has driven the transformation of traditional industry. The proposal for an intelligent ship reflects the development of the shipping industry in the direction of intelligence. Safe maritime transportation is an essential branch of the intelligent ship...

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Veröffentlicht in:Ocean engineering Jg. 283; S. 115040
Hauptverfasser: Yang, Xiao, Han, Qilong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2023
Schlagworte:
Actor–critic
Artificial intelligence
Dynamic obstacle avoidance and path planning
Exploration strategies
Intelligent ship
Intelligent ship
Dynamic obstacle avoidance and path planning
Actor–critic
Exploration strategies
Artificial intelligence
ISSN:0029-8018, 1873-5258
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Zusammenfassung:The rapid development of artificial intelligence has driven the transformation of traditional industry. The proposal for an intelligent ship reflects the development of the shipping industry in the direction of intelligence. Safe maritime transportation is an essential branch of the intelligent ship industry. In this context, applying various intelligent algorithms in ship dynamic obstacle avoidance has attracted the attention and discussion of scholars. Due to the complexity and diversity of ship navigation, environments cannot be described by a definite mathematical model. Reinforcement learning has certain advantages in solving path planning in a complex environment. Traditional mathematical algorithms and swarm intelligence algorithms need mathematical models to constrain boundary conditions when solving complex problems. However, some complex dynamic environments in the real world cannot define mathematical models. This paper optimizes the parameter update and exploration strategies of the actor–critic algorithm in reinforcement learning to improve the algorithm’s performance. Under complex meteorological conditions, improved reinforcement learning is applied to the dynamic ship offshore channel simulation environment to verify the algorithm’s performance. •Ship path planning considered the marine meteorological environment.•Ship applies intelligent decisions in dynamic obstacle avoidance.•The exploration strategy and parameter update of the algorithm have been improved.•This paper constructs a ship intelligent navigation simulation platform to verify the performance.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2023.115040