Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Abstract

Unmanned surface vehicle (USV) is an important autonomous marine vehicle. The safe navigation of USV is directly determined by the local obstacle avoidance because it must avoid real-time local obstacle in the global path planning in a three-dimensional environment. Therefore, efficient algorithms of real-time local obstacle avoidance for USV are a critical issue. In this study, a new threelayered architecture for local obstacle avoidance algorithm was proposed to solve the local obstacle avoidance problem. First, real environment and obstacle models were established in the polar coordinate. The known static path-planning method was conducted based on particle swarm optimization (PSO). Second, the method was integrated with marine rules based on PSO. Third, an obstacle avoidance method under unknown environment was created based on rolling windows. Finally, a simulation experimental platform was developed to verify the feasibility and effectiveness of the aforementioned measure. Result shows that the proposed algorithm can effectively avoid local obstacles of USV at a computation time of less than 2 s. The USV avoids the obstacles smoothly and reaches the desired destination with complex requirements. The simulation results also demonstrate the promising application of the proposed method in studying the path planning of USV. This method can address the issues of real-time local obstacle avoidance of USV.

Research Progress of Test Methods for Unmanned Surface Vehicle in Complex Sea Conditions

“…5) Choose an economic and safe avoidance plan to output and execute. Some scholars have done obstacle avoidance ability test experiments, [8] summarized the research on the progress of autonomous obstacle avoidance ability; [9] based on the improved artificial potential field, initially conducted the obstacle avoidance ability test of the unmanned boat in the artificial field; [10] The whole number was zero, and the partial obstacle avoidance test of the unmanned boat was carried out; [11] Based on the algorithm fusion, the obstacle avoidance test was improved; [12] The test site was expanded to the ocean and the test was performed in a complex environment; [13] The latest test experiment combines the virtual algorithm with the artificial potential field to conduct a more complete obstacle avoidance test for the unmanned boat.…”

Collision avoidance planning for unmanned surface vehicle based on eccentric expansion

“…Therefore, researchers developed different collision avoidance operations for different risk levels on the basis of the rules of COLREGS. 20 In some studies, the rules were divided up into six different encounter situations and 14 avoidance operations, resulting not only in difficulties in the implementation of rules but also in decreased intelligence of USVs.…”

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea 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].…”

“…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.…”

“…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].…”