Cooperative Mission Planning of USVs Based on Intention Recognition

Abstract

To enhance task completion efficiency and quality, the coordination of Unmanned Surface Vehicle (USV) formations in complex environmental situations often requires user intervention. This paper proposes a human-machine collaborative approach for USV mission planning and explores a method for identifying user intervention intentions. A method for recognizing user intention based on intervention style was proposed. The method utilizes the Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) model to recognize intervention style and emphasizes human intention recognition to enhance the ability of USV in complex environments. The method involves modeling continuous intervention operations and incorporating intervention style features to accurately identify user intent. The study proposes a fusion method that combines feature attention, self-attention, and Fusion of Long Short-Term Memory Networks (FLSTMS) to achieve its purpose. Furthermore, it suggests a cooperative mission planning method based on prospect theory, which integrates user risk propensity and identified intentions to optimize planning. Simulation experiments confirm the effectiveness of this approach, highlighting its advantages over traditional methods.