Optimized APF-ACO Algorithm for Ship Collision Avoidance and Path Planning

Abstract

The primary objective of this study is to investigate maritime collision avoidance and trajectory planning in the presence of dynamic and static obstacles during navigation. Adhering to safety regulations is crucial when executing ship collision avoidance tasks. To address this issue, we propose an optimized APF-ACO algorithm for collision avoidance and path planning. First, a ship collision avoidance constraint model is constructed based on COLREGs to enhance the safety and applicability of the algorithm. Then, by introducing factors such as velocity, position, and shape parameters, the traditional APF method is optimized, creating a dynamic APF gradient for collision avoidance decision making in the face of dynamic obstacles. Furthermore, the optimized APF method is integrated with the ant colony optimization algorithm, the latter modified to overcome the inherent local optimality issues in the APF method. Ultimately, validations are conducted in three areas: static avoidance and planning in restricted sea areas, avoidance under conditions of mixed static and dynamic obstacles, and avoidance in situations of multiple ship encounters. These serve to illustrate the feasibility and efficacy of the proposed algorithm in achieving dynamic ship collision avoidance while simultaneously completing path-planning tasks.

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

“…At present, global path planning algorithms mainly include A* [12], Dijkstra [13], and rapidly-exploring random trees (RRTs) [14]. Local path planning algorithms include the artificial potential field algorithm (APF) [15], DWA [16], and deep learning method (DL) [17]. Dijkstra [18] uses a breadth-first search, which has the advantages of simple principles and a small amount of computation, but the computational efficiency is low.…”

Optimal Path Planning Method for Unmanned Surface Vehicles Based on Improved Shark-Inspired Algorithm

“…Zhao et al [14] put forward an adaptive elite GA with fuzzy inference (AEGAfi), which can control the USVs to optimize its global trajectory, and its dynamic behavior conforms to COLREGs. Li et al [15] combined the artificial potential field (APF) with the ACO and proposed an improved APF-ACO algorithm, which overcame the local optimum shortcomings in the APF method, and achieved the path planning and collision avoidance of ships. Hao et al [16] proposed a dynamic fast Q-learning algorithm (DFQL) to plan global USV paths in known marine environments.…”

A Comparative Analysis of Computational Intelligence Methods for Autonomous Navigation of Smart Ships

“…ANN [3] 2023 yes no 1 TS sim. APF-ACO [4] 2023 yes yes up to 3 sim. DRL [5] 2021 yes no up to 10 sim.…”

“…The second largest subgroup of computational intelligence methods presented in the recent literature and applied to the ship collision avoidance problem was swarm intelligence. A hybrid method, combining the use of the APF and Ant Colony Optimization (ACO), was proposed in [4]. The developed algorithm was tested in simulations with both static and dynamic obstacles.…”