Collision Avoidance Algorithm for USV Based on Rolling Obstacle Classification and Fuzzy Rules

Abstract

Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.

USV Dynamic Accurate Obstacle Avoidance Based on Improved Velocity Obstacle Method

“…Many domestic and international researchers have developed and proposed many local path planning algorithms. Common algorithms consist of the artificial potential field method [2][3][4], the dynamic window method [5,6], the velocity obstacle method [7], the vector field histogram method, the fast random tree method, fuzzy logic [8,9], the set-based guidance (SBG) method [10][11][12][13][14][15], etc. Among them, velocity obstacle (VO) is a local path planning method first proposed by Fiorini in 1998 [16].…”

A Dynamic Obstacle Avoidance Method for Unmanned Surface Vehicle under the International Regulations for Preventing Collisions at Sea

“…In recent years, autonomous navigation technology has been widely used in many fields, and the development of unmanned vehicles, unmanned aircraft and unmanned surface vehicles (USVs) is in full swing [1][2][3]. Autonomous ships represented by USVs have received key attention and investment in research and development from maritime powers for their unique advantages [4][5][6][7].…”

Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs

“…In the past few years, USV collision avoidance path planning has attracted extensive attention from both the academia and industry, and the algorithms can be generally classified into two categories. One is to treat USVs as autonomous mobile robots and use model-driven or data-driven approaches for path planning, such as artificial potential fields [ 7 , 8 ], fuzzy logic [ 9 , 10 ] and the velocity obstacle method [ 11 ]. These strategies could be very useful in dynamic situations in open water areas for obstacle avoidance, as USVs are only seen as agile robotic systems without marine navigation restrictions.…”