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

Yanli Chen,Xinyu Hu,Yin Zhan,Jun Liu,Guiqiang Bai

doi:10.1109/access.2021.3062375

Abstract

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.

Highlights

Interest in the path-planning and collision-avoidance problems of unmanned ground vehicles (UGVs), unmanned surface vehicles (USVs), and unmanned aerial vehicles (UAVs) has grown over the past decade [1], [2]
PROBLEM STATEMENT Motivated by the facts and challenges stated above, we propose an improved ant colony optimizationartificial potential field (ACO-artificial potential field (APF)) hybrid algorithm that can describe the shortest time path planning for USVs in an unknown environment without collisions
USV path planning and obstacle avoidance based on an improved ant colony optimization (ACO)-APF hybrid algorithm with adaptive early warning is presented for use in a complex environment

Summary

INTRODUCTION

Interest in the path-planning and collision-avoidance problems of unmanned ground vehicles (UGVs), unmanned surface vehicles (USVs), and unmanned aerial vehicles (UAVs) has grown over the past decade [1], [2]. With the development of artificial intelligence and sensor technology, USVs applications have become increasingly diverse and include scientific research, oceanographic surveys, marine salvage, military use, and military use [3]–[6] These tasks are related to target recognition, path planning, control technology, positioning and navigation technology, communication. Y. Chen et al.: Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm technology [7]. Lazarowska [27] proposed the discrete artificial potential field (DAPF) algorithm so that a USV can effectively avoid static and dynamic obstacles during path planning. Tsou [31] used the ACO algorithm, automatic identification system information, and obstacle avoidance rules for USV dynamic path planning. USV path planning have not been reported in the literature to date

PROBLEM STATEMENT

TRADITIONAL ANT COLONY AND APF ALGORITHM

IMPROVED APF METHOD

Findings

CONCLUSION