Simulation of unmanned ship real-time trajectory planning model based on Q-learning. (3rd November 2021) - Liu, Jindong; Yang, Jie; Guo, Zhiqiang; Cao, Hui; Ren, Yongmei
In view of the challenge in autonomous navigation of unmanned ships where environmental conditions are complicated, this paper proposes a global trajectory planning model with local risk collision avoidance. The model establishes MAKLINK global connectivity map from original sea area, and provides global trajectory planning strategy based on ACO algorithm, and then introduces Q-learning algorithm to realise local risk collision avoidance, thus achieving real-time trajectory planning for unmanned ships. Compared to traditional models, our proposed one reduces level of complexity in environmental modelling, without bringing path uncertainty due to the presence of reinforcement learning and also has a faster trajectory convergence rate and shorter path length. This work would bring meaningful insights to future autonomous navigation research.