A COLREGs-based obstacle avoidance approach for unmanned surface vehicles. (1st December 2018)

• Record Type: Journal Article
• Authors: Wang, Yanlong; Yu, Xuemin; Liang, Xu; Li, Baoan
• Is Part Of: Ocean engineering. Volume 169(2018)
• Page Start: 110
• Page End: 124
• Publication Date: 2018-12-01
• Subjects: Obstacle avoidance -- Unmanned surface vehicle -- Local path planning -- COLREGs -- Normal distribution curve; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2018.09.012
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper reports the preliminary research results of a novel automatic obstacle avoidance approach based on the COLREGs for unmanned surface vehicles (USVs). The approach presented is essentially a path searching-based algorithm called the local normal distribution-based trajectory, which plans viable avoidance trajectories in the presence of both static and dynamic obstacles. The proposed algorithm can generate a COLREGs-compliant suboptimal trajectory based on the bell-shaped curve of normal distribution and extract waypoints for the navigation controller to steer USVs safely. In addition, we discuss three key parameters and present a trajectory replanning strategy to improve the safety and flexibility of our approach. The common overtaking, crossing and head-on collision scenarios are each simulated in experiments. It is shown through simulations that the proposed approach considers multiple factors and can plan paths to avoid obstacles safely and smoothly. A comparison is also made with a reactive path planning algorithm which has been modified to follow the COLREGs. Highlights: We design COLREGs-based collision avoidance rules for USVs. A local normal distribution-based trajectory based on bell-shaped curve of normal distribution is proposed. LNDT algorithm considering multiple factors has better performasnce in safety, predictability, and smoothness. The waypoint prediction method is presented to evaluate the closest distance. We propose six evaluation indicators to assess the performance of obstacle avoidance approaches.