Abstract
For the dynamic collision avoidance problem of an unmanned surface vehicle (USV), a dynamic collision avoidance control method based on an improved cuckoo search algorithm is proposed. The collision avoidance model for a USV and obstacles is established on the basis of the principle of the velocity obstacle method. Simultaneously, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is incorporated in the collision avoidance process. For the improvement of the cuckoo algorithm, the adaptive variable step-size factor is designed to realize the adaptive adjustment of flight step-size, and a mutation and crossover strategy is introduced to enhance the population diversity and improve the global optimization ability. The improved cuckoo search algorithm is applied to the collision avoidance model to obtain an optimal collision avoidance strategy. According to the collision avoidance strategy, the desired evasion trajectory is obtained, and the tracking controller based on PID is used for the Lanxin USV. The experimental results show the feasibility and effectiveness of the proposed collision avoidance method, which provides a solution for the autonomous dynamic collision avoidance of USVs.
Highlights
An unmanned surface vehicle (USV) is an autonomous surface vehicle capable of conducting port patrols and performing special maritime tasks, and has been intensively researched by various countries in recent years [1,2]
Compared with the standard cuckoo search (CS) and particle swarm optimization (PSO) algorithms, the improved cuckoo search (ICS) algorithm performs better in convergence speed, accuracy and global optimization ability, and can meet the calculation requirements of the collision avoidance strategy when applied to USV dynamic collision avoidance
According to the expected course and velocity calculated by the collision avoidance algorithm, the USV will adjust to the expected motion attitude according to its own motion performance constraints, and calculate the expected attitude change at the moment in real time according to the adjusted motion state parameters and position information, so as to realize real-time collision avoidance
Summary
An unmanned surface vehicle (USV) is an autonomous surface vehicle capable of conducting port patrols and performing special maritime tasks, and has been intensively researched by various countries in recent years [1,2]. For the path navigation method of a USV, a chaotic and sharing-learning particle swarm optimization algorithm is developed to solve the extended traveling salesman problem and the nonlinear multiobjective model in [10] These collision avoidance algorithms for USVs include the velocity obstacle method [11], field theory [12,13,14,15], the finite state machine [16], A star [17,18], the LROABRA (local reactive obstacle avoidance based on region analysis) method [15], the VFH+ method [15], the fast marching method [19], and so on. In view of the shortcomings of the cuckoo search algorithm, such as slow convergence speed and low optimization accuracy, the adaptive adjustment of its step size control factor and the introduction of a mutation and crossover strategy are adopted to improve the algorithm, so as to enhance its optimization ability It can obtain the optimal solution and reliably when the improved CS algorithm is applied to the collision avoidance problem. Velocity and contours of dynamic and static obstacles, the decisionmaking information can be provided for the autonomous collision avoidance control of the USV
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