A Fusion Designed Improved Elastic Potential Field Method in AUV Underwater Target Interception

Abstract

In this article, the single autonomous underwater vehicle (AUV) interception problem is analyzed and studied, and a new improved elastic potential field (EPF)-particle swarm-sliding window fusion strategy is proposed to achieve fast interception of underwater moving targets. First, the Sliding Window Fitting Prediction Method is used to fit and predict the trajectory of the target object. To improve the interception efficiency, the Elastic Predictive Interception Method is used to predict the position of the target object at the next moment, and the tracking point is replaced by the prediction point to intercept moving targets. Second, an expansion potential field method is proposed to replace the gradient potential field in the artificial potential field (APF), and the relatively appropriate influence range of the obstacle is found through the particle swarm optimization (PSO) algorithm. Then, the AUV converges gradually from the starting point to the target point through the stepping interception scheme. This method can make up for some shortcomings of the APF method and find a better interception path while making a rapid path planning decision. Simulation results show the interception effect of improved EPF under elastic prediction conditions, and the effectiveness of the proposed method is verified by a comparative analysis.