Method of Emergency Collision Avoidance for Unmanned Surface Vehicle (USV) Based on Motion Ability Database

Abstract

The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

Computational Intelligence in Marine Control Engineering Education

“…According to Lisowski [15], Song [23], Szlapczynska et al [25] and Wang [26], the definition of the collision avoidance problem may be obvious, ignoring data sensitivity which may result from external elements dictated by climatic conditions and the state of the ocean, insufficient knowledge concerning other ships and imprecise proposals of international conflict of law rules (COLREGs).…”

Collision Avoidance Controller for Unmanned Surface Vehicle Based on Improved Cuckoo Search Algorithm

“…u i,j = y i,j rand < P CR or j = j rand x i,j other (22) where rand is the random number between [0, 1], P CR is the crossover probability, j rand is the random index value in [1, D] and D is the dimension number. The better dimension variables of two individuals can be retained in a new individual by cross-recombination between the intermediate individual and the target individual, so that the updated nest location is better.…”

“…Song et al introduced a two-level collision avoidance algorithm and improved the calculation efficiency and accuracy through the particle swarm optimization (PSO) algorithm [6]. For this solution space of the collision avoidance strategy, the objective function is solved by the PSO algorithm in [22]. Guo et al designed an extended chaos and shared learning PSO (CSPSO) algorithm to solve the traveling salesman problem (TSP) and nonlinear multi-objective model in a collision avoidance algorithm [10].…”

COLREGs-Compliant Unmanned Surface Vehicles Collision Avoidance Based on Multi-Objective Genetic Algorithm

“…At present, the determination of collision avoidance timing mainly includes the use of the ship domain to determine the collision avoidance timing [40], the use of the threshold value of ROC to determine the collision avoidance timing [41], and the use of data statistics to determine the collision avoidance timing [42].…”