Formation of MASS Collision Avoidance and Path following Based on Artificial Potential Field in Constrained Environment

Abstract

It is essential to promote the intelligence and autonomy of Maritime Autonomous Surface Ships (MASSs). This study proposed an automatic collision-avoidance method based on an improved Artificial Potential Field (APF) with the formation of MASSs (F-MASSs). Firstly, the navigation environment model was constructed by the S-57 Electronic Navigation Chart (ENC) data in Tianjin Port. The Formation Ship State Parameter (FSSP) definition was proposed for the port environment under multiple constraints that considered the navigation conditions of the MASSs. The formation pattern transformation was settled by changing the formation ship state parameter. Considering the constraints of an ‘unmanned–manned’ encounter situation, the static obstacles, and the design of the channel area improved artificial potential method for the formation. Finally, the simulation experiment was carried out in the sea near Tianjin Port to verify the effectiveness of the algorithm under multiple constraints. The results indicate that the method can satisfy the integrated operation of collision avoidance and path following in a constrained environment, and it can support the application of merchant F-MASS autonomous navigation in the future.

Localized Path Planning for Mobile Robots Based on a Subarea-Artificial Potential Field Model

“…Considers the presence of obstacles between the APO and the goal point, as shown in Figure 3 . An Obstacle Influence Area (OIA) is introduced outside the OEA due to the presence of obstacles [ 33 ], with a radius of R OIA . The numerical range of R OIA is 1.85 R obs to 2.85 R obs .…”

“…At the same time, a series of circular regions were set around the obstacles, as shown in Figure 6 . Starting from the innermost region and moving outward, these circular regions were the Obstacle Expansion Area (OEA), the Obstacle Influence Area (OIA), and the Predicted Potential Field Area (PPFA) [ 28 , 33 , 38 ]. The Predicted Potential Field exerted its influence in the PPFA circular region, enabling the anticipation and control of the AMR’s avoidance angle.…”

Two-level vehicle path planning model for multi-warehouse robots with conflict solution strategies and improved ACO

“…For comparison and analysis, these path planning algorithms are organized in Table 1. Classical methods include artificial potential field algorithms (Chen et al, 2022;Hao et al, 2022;Ma et al, 2022;Souza et al, 2022;Zhao et al, 2023), graph search algorithms (Jin et al, 2023;Li et al, 2022c), and sampling algorithms (Dian et al, 2022;Ding et al, 2023;Ma et al, 2023). With the development of computational techniques, metaheuristic optimization algorithms have emerged as an efficient path planning method for highdimensional complex problems based on genetic evolution mechanisms and cluster foraging migration mechanisms theories.…”