COLREGS-based Path Planning for Ships at Sea Using Velocity Obstacles

Zhang, Wenjun; Yan, Chengyong; Lyu, Hongguang; Wang, Pinglin; Xue, Zongyao; Li, Ze-Hua; Bai, Xia

doi:10.1109/access.2021.3060150

Cited by 26 publications

(10 citation statements)

References 20 publications

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“…In total, 67% out of 25 algorithms were verified by simulations only, 21% by field experiments and 13% by both simulations and real experiments. Methods characterized by low run time, such as [33,34,36,38,39,43,45,48,54], can be regarded as the most practical approaches, especially for the use in USV motion control system. Methods verified by field tests also seem to be the most promising, such as [34,36,39,48,60].…”

Section: Discussionmentioning

confidence: 99%

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Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing

Lazarowska

2021

Remote Sensing

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The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing.

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Section: Discussionmentioning

confidence: 99%

“…In 2021 [33], authors introduced an approach utilizing the velocity obstacles (VO) method. DCPA and TCPA measures are used in order to assess the collision risk.…”

Section: Deterministic Approachesmentioning

confidence: 99%

Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing

Lazarowska

2021

Remote Sensing

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“…The process of experience replay is as follows: First, a quad is randomly extracted from the experience replay array and record it as(s j , a j , r j , s j+1 ), Qnow , Qnew . Second, the element of Qnow at position (s j , a j ) is recorded as (6). Third, the maximum value of line s j+1 of Qnow is calculated as (7).…”

Section: A the Original Q-learning Algorithmmentioning

confidence: 99%

Improved Q-Learning Applied to Dynamic Obstacle Avoidance and Path Planning

2022

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Due to the complexity of interactive environments, dynamic obstacle avoidance path planning poses a significant challenge to agent mobility. Dynamic path planning is a complex multi-constraint combinatorial optimization problem. Some existing algorithms easily fall into local optimization when solving such problems, leading to defects in convergence speed and accuracy. Reinforcement learning has certain advantages in solving decision sequence problems in complex environments. A Q-learning algorithm is a reinforcement learning method. In order to improve the value evaluation of the algorithm in solving practical problems, this paper introduces the priority weight into the Q-learning algorithm. The improved algorithm is compared with existing algorithms and applied to dynamic obstacle avoidance path planning. Experiments show that the improved algorithm dramatically improves the convergence speed and accuracy and increases the value evaluation. The improved algorithm finds the shortest path of 16 units in 27 seconds.

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“…Zhang et al. proposed the idea of changing speed to avoid collision, which not only avoids blind obedience to COLREGS rules, but also reduces deviation from planned route [13]. Liu et al.…”

Section: B Local Dynamic Collision Avoidancementioning

confidence: 99%

PE-A* Algorithm for Ship Route Planning Based on Field Theory

Liu

Wang

2022

IEEE Access

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Route planning is the key to safe, efficient and intelligent navigation of maritime traffic. Autonomous route planning is a complex optimization problem, which requires both global route planning and local collision avoidance. In this paper, we propose an optimization algorithm which can consider both global route planning and local collision avoidance. Firstly, nonlinear constraint optimization models of obstacle limitation, safe water depth limitation and ship steering limitation are established. Then, the PE-A * algorithm and route planning framework are proposed by using potential energy field to accurately express the environment. Finally, the safety and feasibility of the route planned by PE-A * algorithm are discussed through simulation experiments. Simulation results show that PE-A * algorithm can realize both global route planning and local collision avoidance, and the safety and feasibility of the planned route are greatly improved. From the perspective of potential energy, this work proposes an automatic route planning algorithm and establishes a flexible mathematical model, which can add fuel consumption, time and other related engineering requirements into the model to plan the optimal route meeting the requirements.INDEX TERMS Ship, potential energy, A * algorithm, route planning.

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COLREGS-based Path Planning for Ships at Sea Using Velocity Obstacles

Cited by 26 publications

References 20 publications

Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing

Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing

Improved Q-Learning Applied to Dynamic Obstacle Avoidance and Path Planning

PE-A* Algorithm for Ship Route Planning Based on Field Theory

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