A novel collision avoidance (CA) algorithm was proposed based on the modified artificial potential field (APF) method, to construct a practical ship automatic CA system. Considering the constraints of both the International Regulations for Preventing Collisions at Sea (COLREGS) and the motion characteristics of the ship, the multi-ship CA algorithm was realized by modifying the repulsive force model in the APF method. Furthermore, the distance from the closest point of approach-time to the closest point of approach (DCPA-TCPA) criterion was selected as the unique adjustable parameter from the perspective of navigation practice. Collaborative CA experiments were designed and conducted to validate the proposed algorithm. The results of the experiments revealed that the actual DCPA and TCPA agree well with the parameter setup that keeps the ship at a safe distance from other ships in complex encountering situations. Consequently, the algorithm proposed in this study can achieve efficient automatic CA with minimal parameter settings. Moreover, the navigators can easily accept and comprehend the adjustable parameters, enabling the algorithm to satisfy the demand of the engineering applications.

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The accurate environment potential field (EPF) modeling method and highly efficient collision avoidance (CA) approach are key technologies for maritime autonomous surface ships (MASS). A novel and accurate environment potential field (EPF) model is proposed using electronic navigation chart (ENC) face objects to describe different types of navigable and non-navigable areas, and an improved artificial potential field (APF) method is presented to realize collaborative CA and obstacle avoidance (OA). Implicit equations of complex-shaped face objects were constructed based on R-function theory, and the discrete-convex hull method was introduced to realize automatic EPF modeling. Collaborative CA and OA experiments in restricted waters were conducted on a ship handling simulator. The results show that the improved APF method can obtain a robust and deterministic collision-free path under different weather conditions and in restricted waters, and the track zone width remains within 0.1 nm. The proposed face object EPF model is efficient and accurate, even with numerous vertices and complex shapes, and can drive the ship apart at a relatively safe distance in accordance with the recommended CA parameter. We present a practical CA approach and an effective EPF modeling method for APF-based ship path planning.

Several studies have been conducted on collision avoidance (CA) and path planning for maritime autonomous surface ships (MASS) based on artificial potential field (APF) and electronic navigation chart (ENC) data. However, to date, accurate, highly efficient, and automatic modelling of complicated geometry environment potential fields (EPFs) has not been realised. In this study, an accurate EPF model is established using ENC data to describe different types of obstacles, navigable areas, and non-navigable areas. The implicit equations of complex polygons are constructed based on the R-function theory, and the discrete-convex hull method is introduced to realise the automatic modelling of EPF. Moreover, collaborative CA and obstacle avoidance (OA) experiments are designed and conducted in a simulated environment and based on the ENC data. The results show that the proposed EPF modelling method is accurate, reliable, and time-efficient even with numerous ENC data and complex shapes owing to the R-function representation for geometric objects and discrete-convex hull method. The combination of improved APF and EPF models is proven to be effective for CA and OA. This paper presents a practical EPF modelling approach for APF-based ship path planning.