A Rule-based Heuristic Method for COLREGS-compliant Collision Avoidance for an Unmanned Surface Vehicle

Campbell, Sable; Naeem, Wasif

doi:10.3182/20120919-3-it-2046.00066

Cited by 35 publications

(25 citation statements)

References 5 publications

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“…The common way of route planning for USV (A (Campbell, S. & Naeem, W., 2012), B (Niu, H., et al, 2016), C (Naeem, W., 2016), D (Baidu website gallery))…”

Section: Figurementioning

confidence: 99%

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The review unmanned surface vehicle path planning: Based on multi-modality constraint

et al. 2020

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The essence of the path planning problems is multi-modality constraint. However, most of the current literature has not mentioned this issue. This paper introduces the research progress of path planning based on the multi-modality constraint. The path planning of multi-modality constraint research can be classified into three stages in terms of its basic ingredients (such as shape, kinematics and dynamics et al.): Route Planning, Trajectory Planning and Motion Planning. It then reviews the research methods and classical algorithms, especially those applied to the Unmanned Surface Vehicle (USV) in every stage. Finally, the paper points out some existing problems in every stage and suggestions for future research.

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“…The common way of route planning for USV (A (Campbell, S. & Naeem, W., 2012), B (Niu, H., et al, 2016), C (Naeem, W., 2016), D (Baidu website gallery))…”

Section: Figurementioning

confidence: 99%

“…The regulations are introduced to normalize the planned path. Researchers(Yanshuang, 2010;Huai Yang, 2016;Campbell, 2012) firstly planned out a global path by A* algorithm in the macromodality constraint environment. And then based on COLREGS, they classified collisions into different situations and worked out different behavior to avoid each situation.…”

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confidence: 99%

The review unmanned surface vehicle path planning: Based on multi-modality constraint

et al. 2020

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“…It can safely avoid obstacles and independently plan an optimal trajectory from the known starting point to the target point. Unmanned ships often face complex and variable navigational Campbell, S et al [22] showed a USV real-time path planning method that was based on improved A* algorithm, which is integrated with decision-making framework and combined with COLREGS. The results show that the method realizes real-time path planning of the USV in complex navigation environment.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning

Guo

Zhang

Zheng

et al. 2020

Sensors

153

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Deep reinforcement learning (DRL) has excellent performance in continuous control problems and it is widely used in path planning and other fields. An autonomous path planning model based on DRL is proposed to realize the intelligent path planning of unmanned ships in the unknown environment. The model utilizes the deep deterministic policy gradient (DDPG) algorithm, through the continuous interaction with the environment and the use of historical experience data; the agent learns the optimal action strategy in a simulation environment. The navigation rules and the ship’s encounter situation are transformed into a navigation restricted area, so as to achieve the purpose of planned path safety in order to ensure the validity and accuracy of the model. Ship data provided by ship automatic identification system (AIS) are used to train this path planning model. Subsequently, the improved DRL is obtained by combining DDPG with the artificial potential field. Finally, the path planning model is integrated into the electronic chart platform for experiments. Through the establishment of comparative experiments, the results show that the improved model can achieve autonomous path planning, and it has good convergence speed and stability.

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“…In the development of an obstacle avoidance system for USVs that is compliant with the COLREGs (International Regulations for Preventing Collision at Sea), path planning is extended to use the A* algorithm to improve its efficiency (Loe, 2008). The safe, optimal, and feasible paths produced by the path planning technique have been implemented successfully (Campbell and Naeem, 2012). When a USV operates in an obstacle field, the distance from the USV to the obstacles, the computational time, and the smoothness of the final path are essential factors that are employed to assess the ability of the USV (Mohammadi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

Yoon

Ryu

et al. 2019

J. Ocean Eng. Technol.

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Efficient path planning is essential for unmanned surface vehicle (USV) navigation. The A* algorithm is an effective algorithm for identifying a safe path with optimal distance cost. In this study, a modified version of the A* algorithm is applied for planning the path of a USV in a static and dynamic obstacle environment. The current study adopts the A* approach while maintaining a safe distance between the USV and obstacles. Two important parameters-path length and computational time-are considered at various start times. The results demonstrate that the modified approach is effective for obstacle avoidance by a USV that is compliant with the International Regulations for Preventing Collision at Sea (COLREGs).This is an open access article distributed under the terms of the creative commons attribution non-commercial license (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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A Rule-based Heuristic Method for COLREGS-compliant Collision Avoidance for an Unmanned Surface Vehicle

Cited by 35 publications

References 5 publications

The review unmanned surface vehicle path planning: Based on multi-modality constraint

The review unmanned surface vehicle path planning: Based on multi-modality constraint

An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

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