ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 水面无人艇自适应危险规避决策过程收敛性分析

1. 1(哈尔滨工程大学计算机科学与技术学院 哈尔滨 150001);2(大连民族学院机电信息学院 辽宁大连 116600);3(武汉第二船舶设计研究所 武汉 430064) (tpinheu@163.com)
• 出版日期: 2014-12-01
• 基金资助:
基金项目：国家自然科学基金项目(60975071,61100005,60975019)

### Convergence Analysis of Adaptive Obstacle Avoidance Decision Processes for Unmanned Surface Vehicle

Zhang Rubo1,2, Tang Pingpeng1,3, Yang Ge1, Li Xueyao1, Shi Changting1

1. 1(College of Computer Science and Technology, Harbin Engineering University, Harbin 150001); 2(College of Electromechanical and Information Engineering, Dalian Nationalities University, DaLian, Liaoning 116600); 3(Wuhan Second Ship Design and Research Institute, Wuhan 430064)
• Online: 2014-12-01

Abstract: Unmanned surface vehicle (USV) is a kind of important marine autonomous robots, which has been studied and applied to practice gradually. However, the autonomy of USV is still restricted by the performance of autonomous navigation technology. Especially, the problem of adaptive obstacle avoidance in complicated sea-state marine environments needs to be solved urgently. In the paper, an adaptive avoidance decision process model is proposed for USV to solve the problem of obstacle avoidance in complicated sea-state marine environments. By analyzing the disturbance factors from complicated sea-state marine environments, the model is constructed on the basis of Sarsa on-policy reinforcement learning algorithm. By setting the GLIE (greedy in the limit and infinite exploration) as the action exploration, the convergence of the adaptive avoidance decision process has been proved. The convergence shows that the action can converge to the optimal action strategy with the probability value of one. The proved result demonstrates that the performance of obstacle avoidance of USV in the complicated sea-state marine environment can be enhanced under the action of on-policy reinforcement learning algorithm.