Unmanned surface vehicle adaptive decision model for changing weather

Abstract

The autonomous decision-making capability of unmanned surface vehicles (USV) is the basis for many tasks. Most of the works ignore the variability of the scene. For example, traditional decision-making methods are not adaptable to changing weather that a USV is likely to encounter. In order to solve the low adaptability problem of a USV using single decision model in changing weather, we propose an adaptive model of USV based on human memory cognitive process. The USV first stores the perceived weather features in sensory memory. Then, it combines weather characteristics with prior knowledge to classify the weather in perceptual associative memory. Finally, USV calls different decision models stored in long-term memory based on the current weather category to make the decision. Simulated experiments are carried out on USV obstacle avoidance decision task in Unity3D. Experiments show that our model performs better than using only a single decision model.