Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm With Adaptive Early-Warning

Path planning is important to the efficiency and navigation safety of USV autonomous operation offshore. To improve path planning, this study proposes the improved ant colony optimization-artificial potential field (ACO-APF) algorithm, which is based on a grid map for both local and global path planning of USVs in dynamic environments. The improved ant colony optimization (ACO) mechanism is utilized to search for a globally optimal path from the starting point to the endpoint for a USV in a grid environment, and the improved artificial potential field (APF) algorithm is subsequently employed to avoid unknown obstacles during USV navigation. The primary contributions of this article are as follows: (1) this article proposes a new heuristic function, pheromone update rule, and dynamic pheromone volatilization factor to improve convergence and mitigate finding local optima with the traditional ant colony algorithm; (2) we propose an equipotential line outer tangent circle and redefine potential functions to eliminate goals unreachable by nearby obstacles (GNRONs) and local minimum problems, respectively; (3) to adapt the USV to a complex environment, this article proposes a dynamic early-warning step-size adjustment strategy in which the moving distance and safe obstacle avoidance range in each step are adjusted based on the complexity of the surrounding environment; (4) the improved ant colony optimization algorithm and artificial potential field algorithm are effectively combined to form the algorithm proposed in this article, which is verified as an effective solution for USV local and global path planning using a series of simulations. Finally, in contrast to most papers, we successfully perform field experiments to verify the feasibility and effectiveness of the proposed algorithm.

Automated summary

This study proposes an improved ACO-APF algorithm for path planning of USVs, which combines ant colony optimization and artificial potential field algorithms to enhance efficiency and safety in dynamic environments.