Research on autonomous collision avoidance of merchant ship based on inverse reinforcement learning

International Journal of Advanced Robotic Systems

article

Mao Zheng¹  Shuo Xie²  Xiumin Chu¹  Tianquan Zhu¹  Guohao Tian¹ 

[1] National Engineering Research Center for Water Transportation Safety, Wuhan University of Technology;China Classification Society;School of Energy and Power Engineering, Wuhan University of Technology

关键词: Inverse reinforcement learning;    collision avoidance;    cross entropy;    projection;    merchant ship;   

DOI  :  10.1177/1729881420969081

学科分类：社会科学、人文和艺术（综合）

来源: InTech

【 摘 要 】

To learn the optimal collision avoidance policy of merchant ships controlled by human experts, a finite-state Markov decision process model for ship collision avoidance is proposed based on the analysis of collision avoidance mechanism, and an inverse reinforcement learning (IRL) method based on cross entropy and projection is proposed to obtain the optimal policy from expert’s demonstrations. Collision avoidance simulations in different ship encounters are conducted and the results show that the policy obtained by the proposed IRL has a good inversion effect on two kinds of human experts, which indicate that the proposed method can effectively learn the policy of human experts for ship collision avoidance.

【 授权许可】

CC BY