Abstract
This paper proposes an intention understanding algorithm (KDI) based on an elderly service robot, which combines Neural Network with a seminaive Bayesian classifier to infer user’s intention. KDI algorithm uses CNN to analyze gesture and action information, and YOLOV3 is used for object detection to provide scene information. Then, we enter them into a seminaive Bayesian classifier and set key properties as super parent to enhance its contribution to an intent, realizing intention understanding based on prior knowledge. In addition, we introduce the actual distance between the users and objects and give each object a different purpose to implement intent understanding based on object-user distance. The two methods are combined to enhance the intention understanding. The main contributions of this paper are as follows: (1) an intention reasoning model (KDI) is proposed based on prior knowledge and distance, which combines Neural Network with seminaive Bayesian classifier. (2) A set of robot accompanying systems based on the robot is formed, which is applied in the elderly service scene.
Highlights
Many researches have been conducted on multimodal information fusion technology, and multimodal information fusion is a technology to integrate information from different sources [2]
We greatly improve the efficiency of high-level task recognition by combining the advantages of Neural Network and the advantages of the probabilistic model
In the intention understanding task, we add the system active response process, which greatly improves the intelligence of the system
Summary
If adding multimodal information will introduce a large number of parameters and feature level information fusion is not easy to adjust recognition results frequently and has poor flexibility, it is not suitable for advanced intention understanding tasks. [10] proposed an integrated probability-based decision framework for robots to infer the role of humans in a particular task It combines Neural Network and probability model. YOLOV3 is favored in many tasks with high edge computing and real-time requirements It is widely used in environment detection to provide scene information [16, 17]. We greatly improve the efficiency of high-level task recognition by combining the advantages of Neural Network and the advantages of the probabilistic model. We will introduce the process of intention reasoning based on Bayesian and distance and the basis of intention classification after intention fusion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
