Data-driven Autonomous Decision-making Method for the Effective Position of AUV Torpedo Attacks

Abstract

Autonomous decision-making capability is a distinctive feature that distinguishes unmanned undersea vehicles from manned platforms. This capability is characterized by a short autonomous decision-making time, high decision-making accuracy, and executable decision-making solutions. In this study, an autonomous decision-making method that combines operational simulation with ensemble learning was proposed to overcome the shortcomings of traditional effective position decision-making methods with regard to the attack effect and decision speed when an autonomous undersea vehicle(AUV) launches an attack against a surface ship using an acoustic homing torpedo. First, many basic experimental data sets were obtained by optimizing the probability of acoustic homing torpedo detection targets. Subsequently, a detection probability attack judgment threshold was designed to convert the AUV’s effective position decision-making into a binary classification problem and form the classification experimental data. Finally, the classification performance of the support vector machine, random forest, and XGBoost were analyzed and it was concluded that ensemble learning is more suitable for this unbalanced sample classification problem. Furthermore, the adaptability of this model under multiple task thresholds and its generalization ability in complex marine environments were tested. The test results showed that this method can meet the AUV’s autonomous attack decision-making requirements and significantly accelerate the decision-making speed while ensuring the effectiveness of the torpedo attack. Therefore, this study provides a reference for research on the attack planning module of equipment.

Keywords

• autonomous undersea vehicle
• autonomous decision-making capability
• ensemble learning
• torpedo