Ships Collision Avoidance Based on Quadrangle Ship Domain and Reciprocal Velocity Obstacle

Lu Ying, Zhao Yuetao, Shi Yu, Wang Likun, Gu Miaoqi, Ou Mingjie, Luo Feixue, Xu Jianhua
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:280-289, 2024.

Abstract

Navigating the narrow and congested waters of the Yangtze River in China poses a significant challenge, leading to frequent ship-ship and ship-buoy collisions. In most cases of collisions between ships and buoys, the ship often hits and runs. This paper introduces a novel collision-avoidance decision method that employs the RVO (Reciprocal Velocity Obstacles) and QSD (Quantitative Ship Domain) to enable dynamic obstacle avoidance for ship-to-buoy and ship-to-ship, which complies with conventions on the international regulation for preventing collision at sea. QSD can dynamically adjust the ship domain model according to different speeds to address different encounter situations. The combination of RVO and QSD combines the dynamic ship domain with the obstacle avoidance algorithm, which makes the water transport safer than the traditional obstacle avoidance algorithm. In addition, this paper also compares the effects of VO (Velocity obstacle) and RVO, and the results indicate that RVO has smoother obstacle avoidance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-ying24a, title = {Ships Collision Avoidance Based on Quadrangle Ship Domain and Reciprocal Velocity Obstacle}, author = {Ying, Lu and Yuetao, Zhao and Yu, Shi and Likun, Wang and Miaoqi, Gu and Mingjie, Ou and Feixue, Luo and Jianhua, Xu}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {280--289}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/ying24a/ying24a.pdf}, url = {https://proceedings.mlr.press/v245/ying24a.html}, abstract = {Navigating the narrow and congested waters of the Yangtze River in China poses a significant challenge, leading to frequent ship-ship and ship-buoy collisions. In most cases of collisions between ships and buoys, the ship often hits and runs. This paper introduces a novel collision-avoidance decision method that employs the RVO (Reciprocal Velocity Obstacles) and QSD (Quantitative Ship Domain) to enable dynamic obstacle avoidance for ship-to-buoy and ship-to-ship, which complies with conventions on the international regulation for preventing collision at sea. QSD can dynamically adjust the ship domain model according to different speeds to address different encounter situations. The combination of RVO and QSD combines the dynamic ship domain with the obstacle avoidance algorithm, which makes the water transport safer than the traditional obstacle avoidance algorithm. In addition, this paper also compares the effects of VO (Velocity obstacle) and RVO, and the results indicate that RVO has smoother obstacle avoidance.} }
Endnote
%0 Conference Paper %T Ships Collision Avoidance Based on Quadrangle Ship Domain and Reciprocal Velocity Obstacle %A Lu Ying %A Zhao Yuetao %A Shi Yu %A Wang Likun %A Gu Miaoqi %A Ou Mingjie %A Luo Feixue %A Xu Jianhua %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-ying24a %I PMLR %P 280--289 %U https://proceedings.mlr.press/v245/ying24a.html %V 245 %X Navigating the narrow and congested waters of the Yangtze River in China poses a significant challenge, leading to frequent ship-ship and ship-buoy collisions. In most cases of collisions between ships and buoys, the ship often hits and runs. This paper introduces a novel collision-avoidance decision method that employs the RVO (Reciprocal Velocity Obstacles) and QSD (Quantitative Ship Domain) to enable dynamic obstacle avoidance for ship-to-buoy and ship-to-ship, which complies with conventions on the international regulation for preventing collision at sea. QSD can dynamically adjust the ship domain model according to different speeds to address different encounter situations. The combination of RVO and QSD combines the dynamic ship domain with the obstacle avoidance algorithm, which makes the water transport safer than the traditional obstacle avoidance algorithm. In addition, this paper also compares the effects of VO (Velocity obstacle) and RVO, and the results indicate that RVO has smoother obstacle avoidance.
APA
Ying, L., Yuetao, Z., Yu, S., Likun, W., Miaoqi, G., Mingjie, O., Feixue, L. & Jianhua, X.. (2024). Ships Collision Avoidance Based on Quadrangle Ship Domain and Reciprocal Velocity Obstacle. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:280-289 Available from https://proceedings.mlr.press/v245/ying24a.html.

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