A novel method of unmanned surface vehicle autonomous cruise

Abstract

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

“…Kim et al suggested a grid map-based on the Theta* algorithm to create paths in real-time, considering both the yaw rate and course angle of USV [4]. Combining global path planning with local path planning, the shortcut Dijkstra algorithm and modified artificial potential field (APF) algorithm were used for the vessel's navigation in [5]. After, [6] improved the global path planning, that is the artificial potential field-ant colony optimization (APF-ACO) obstacle-avoidance algorithm.…”

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

“…In addition, although many works in the literature (see e.g., [6,7,9]), use satellite maps for environmental information, this method is incomplete for describing the invisible sea environment, such as reefs, ocean depths, and so on. Thus in this paper, environmental information is obtained from the electronic chart, which can display this invisible information in detail.…”

“…Although these algorithms have good real-time performance, they are difficult to use in complex and irregular environments. To solve this problem, some scholars use the hierarchical structure to combine global path planning and local path re-planning [6,11,12], and some try to improve the traditional path planning algorithm, using for example Rule-based Repairing A* [8]. Liu et al selected the fast marching method (FMM)-based path planning algorithm [20].…”

“…Hence, path re-planning (collision avoidance) is proposed to meet the demands of real-time planning or avoidance under dynamic environments. These algorithms include the rolling windows method [11], artificial potential field (APF) [6,12], velocity obstacle (VO) [15,16], local reactive obstacle avoidance [17], optimal reciprocal collision avoidance [18], dynamical virtual ship (DVS) [19], and so on. Although these algorithms have good real-time performance, they are difficult to use in complex and irregular environments.…”

“…These include Dijkstra's algorithm [6], A* [7,8], Theta* [9], the Voronoi diagram [10], particle swarm optimization (PSO) [11], ant colony optimization (ACO) [12,13], the genetic algorithm (GA) [14], and so on. Recently, considering the turning capacity of USVs, Yang and Tseng used the finite Angle A* algorithm (FAA*), which is used to determine safer and suboptimal paths for USVs on satellite thermal images [7].…”