Unmanned surface vehicle adaptive decision model for changing weather Online publication date: Mon, 15-Mar-2021
by Han Zhang; Xinzhi Wang; Xiangfeng Luo; Shaorong Xie; Shixiong Zhu
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 1, 2021
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook