Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Zhang, Liang; Mou, Junmin; Chen, Pengfei; Li, Mengxia

doi:10.3390/jmse9070761

Cited by 32 publications

(13 citation statements)

References 37 publications

(54 reference statements)

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“…They introduced a self-learning A* algorithm to handle scenarios involving quadratic programming and situations where local paths cannot be generated. Zhang, L. et al (2021) employed an enhanced artificial potential field method for global path planning and integrated an improved velocity obstacle (VO) method with the Closest Point of Approach (CPA) and COLREGS to derive local paths. This approach facilitates the identification of both global and local paths to avoid target ships.…”

Section: Global and Local Hybrid Path Planning Algorithmmentioning

confidence: 99%

Review of Intelligent Ship Path Planning Algorithms

Song,

Cao

2024

FMS

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Intelligent ship route planning has become an important research direction in the field of shipping in recent years. This paper first provides an overview of the basic theory of intelligent ship route planning. Secondly, various intelligent ship route planning algorithms are introduced, including methods based on A* algorithm, artificial potential field algorithm, RRT algorithm, and reinforcement learning. These algorithms analyze information such as ocean environment, predict sea conditions and traffic conditions, and consider ship dynamics and navigation safety constraints to provide efficient and safe navigation routes for ships. Finally, this paper points out the key issues and future development directions in intelligent ship route planning. The continuous innovation and application of intelligent ship route planning algorithms will provide more intelligent and efficient ship transportation services for the shipping industry, promoting the sustainable development of the shipping industry.

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Section: Global and Local Hybrid Path Planning Algorithmmentioning

confidence: 99%

Review of Intelligent Ship Path Planning Algorithms

Song,

Cao

2024

FMS

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“…Separate the real-time state values of each from the desired values. Then, we can achieve Equation (18).…”

Section: Stability Verification Of the Uav Swarm Systemmentioning

confidence: 99%

“…First, it does not consider the collaborative relationships between UAVs. The second issue lies in the shortcomings of the method, such as local extrema and unreachable points [18].…”

Section: Introductionmentioning

confidence: 99%

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

Zhang,

Chen,

Chen

et al. 2024

Mathematics

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For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader’s attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient “few controlling many” paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms.

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“…Guo et al [19] constructed an autonomous path planning model based on Deep Reinforcement Learning (DRL) and integrated it into the electronic chart platform for experiments to verify the effectiveness and stability of the model. Zhang et al [20] proposed a hybrid approach for autonomous ship path planning based on global paths and local paths. The typical artificial potential field method (APF) and velocity obstacle method (VO) are improved.…”

Section: Introductionmentioning

confidence: 99%

A Method for Coastal Global Route Planning of Unmanned Ships Based on Human-like Thinking

Wang,

Wang

et al. 2024

JMSE

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Global route planning has garnered global scholarly attention as a crucial technology for ensuring the safe navigation of intelligent ships. The comprehensive influence of time-varying factors such as water depth, prohibited areas, navigational tracks, and traffic separation scheme (TSS) on ship navigation in coastal global route planning has not been fully considered in existing research, and the study of route planning method from the perspective of practical application is still needed. In this paper, a global route planning method based on human-like thinking for coastal sailing scenarios is proposed. Based on the historical route’s information, and taking into full consideration those time-varying factors, an abnormal waypoint detection and correction method is proposed to make the planned route conform to relevant regulations of coastal navigation and the common practices of seafarers as much as possible, and better meet the coastal navigation needs of unmanned ships. Taking the global route planning of “ZHIFEI”, China’s first autonomous navigation container ship, as an example, the validity and reliability of the proposed method are verified. Experimental findings demonstrate the efficacy of the proposed method in global route planning for coastal navigation ships. The method offers a solid theoretical foundation and technical guidance for global route planning research of unmanned ship.

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Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Cited by 32 publications

References 37 publications

Review of Intelligent Ship Path Planning Algorithms

Review of Intelligent Ship Path Planning Algorithms

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

A Method for Coastal Global Route Planning of Unmanned Ships Based on Human-like Thinking

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