A Study on Ship Route Generation with Deep Q Network and Route Following Control

• 영문명: A Study on Ship Route Generation with Deep Q Network and Route Following Control
• 발행기관: 한국항해항만학회
• 저자명: 김민규 이형탁
• 간행물 정보: 『한국항해항만학회지』제47권 제2호, 75~84쪽, 전체 10쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2023.04.30

영문 초록

Ships need to ensure safety during their navigation, which makes route determination highly important. It must be accompanied by a route following controller that can accurately follow the route. This study proposes a method for automatically generating the ship route based on deep reinforcement learning algorithm and following it using a route following controller. To generate a ship route, under keel clearance was applied to secure the ship's safety and navigation chart information was used to apply ship navigation related regulations. For the experiment, a target ship with a draft of 8.23 m was designated. The target route in this study was to depart from Busan port and arrive at the pilot boarding place of the Ulsan port. As a route following controller, a velocity type fuzzy PID controller that could compensate for the limitation of a linear controller was applied. As a result of using the deep Q network, a route with a total distance of 62.22 km and 81 waypoints was generated. To simplify the route, the Douglas-Peucker algorithm was introduced to reduce the total distance to 55.67 m and the number of way points to 3. After that, an experiment was conducted to follow the path generated by the target ship. Experiment results revealed that the velocity type fuzzy PID controller had less overshoot and fast settling time. In addition, it had the advantage of reducing the energy loss of the ship because the change in rudder angle was smooth. This study can be used as a basic study of route automatic generation. It suggests a method of combining ship route generation with the route following control.

목차

1. Introduction

2. Dynamic ship model

3. Design of the route following controller

4. Route generation of the ship

5. Route following control

6. Conclusion

Acknowledgements

References

키워드

ship route generation coastal navigation route following control deep Q network reinforcement learning