Unmanned aerial vehicle operator state evaluation method based on multi-sensor measurement and neural network learning

A neural network learning and multi-sensor technology, applied in the field of UAV operator status assessment based on multi-sensor measurement and neural network learning, can solve misjudgment and misoperation, ground station operator workload and increased operational difficulty And other issues

Active Publication Date: 2017-02-22
NAT UNIV OF DEFENSE TECH
View PDF2 Cites 28 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The ground station operator still has the final decision to control the aircraft during the execution of the mission. The UAV system is still a human-in-the-loop system. Its combat

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Unmanned aerial vehicle operator state evaluation method based on multi-sensor measurement and neural network learning
  • Unmanned aerial vehicle operator state evaluation method based on multi-sensor measurement and neural network learning
  • Unmanned aerial vehicle operator state evaluation method based on multi-sensor measurement and neural network learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0053] The present invention aims to provide a UAV operator state assessment method based on multi-sensor measurement and neural network learning, and establishes a mapping relationship with the above-mentioned sensor features and semantics, thereby estimating the operator's decision-making level. This method is convenient for grasping the real-time status of the operator more intuitively, estimating whether the operator is currently suitable for the task, and making corresponding adjustments to the task. Due to the learning method of neural network, the feature space of different operators can be established, so it is suitable for various levels of drone operators with different proficiency. The specific implementation of the method will be described below in conjunction with the accompanying drawings.

[0054] Such as figure 1 Shown is a f...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an unmanned aerial vehicle operator state evaluation method based on multi-sensor measurement and neural network learning. A mapping relation between a sensor characteristic and a semanteme is established, thereby estimating a decision grade of an operator. The method has advantages of facilitating more visual mastering of real-time operator state, estimating whether the operator is suitable for a task, and performing corresponding adjustment on the task. Because a neural network learning method is utilized, a characteristic space of different operators can be established. Therefore, the unmanned aerial vehicle operator state evaluation method is suitable for various grades of unmanned aerial vehicle operators with different proficiencies.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle systems, in particular to a method for assessing the state of an unmanned aerial vehicle operator based on multi-sensor measurement and neural network learning. Background technique [0002] With the improvement of the automation of UAVs, the operator's control of UAVs has changed from low-level behavior-based control to high-level knowledge-based control. This knowledge-based control is supervisory control. In supervisory control, flight control is done automatically by the system, and the operator is mainly responsible for high-level mission management and load management. Traditional command and control of a drone often requires multiple operators, while supervisory control makes it possible for a single operator to control multiple drones. On the one hand, the improvement of platform automation provides technical support for this transformation. On the other hand, Network-cent...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/06G06N3/02G06K9/00A61B5/01A61B5/024
CPCA61B5/01A61B5/024G06N3/02G06Q10/0639G06V40/174G06V40/20G06V40/10G06V40/18
Inventor 牛轶峰钟志伟尹栋王祥科李杰相晓嘉贾圣德王菖
Owner NAT UNIV OF DEFENSE TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap