Inference of User's Intention for Human - Robot Cooperation Based on Machine Vision

An intention prediction algorithm for natural human-computer interaction based on machine vision is proposed in this paper. Firstly, the motion data of human skeletal feature point is acquired. Then, the motion data and the real-time interactive image are coupled through data processing. Meanwhile, an intention recognition model for natural human-computer interaction is built based on target feature extraction. The dominant feature weight of the operator intention is distributed by hierarchical method. A parallel scheme is adopted in this algorithm for operator intention recognition. Through experiment and data analysis, the algorithm is proved to be reliable and instrumental for improving the efficiency of natural human-computer interaction. Introduction The home service robots which are commonly used for the company of the elderly and the disabled are required to complete the tasks of transferring items, opening doors, identifying and predicting human’s intentions, as well as contributing to the cooperation with human and helping the elderly and disabled people with their daily work. A series of methods have been put forward through massive research work by scholars to predict the user's operation intention and facilitate with operation on service robots. For example, De Carli D. and Zhu C. proposed methods to infer the user's intention by the Hidden Markov Model(HMM)[1,2] . J. Elfring adopted the Growing Hidden Markov Model to forecast human’s intended position[3]. Karim A. Tahboub established IntentionBehavior-State model and introduced dynamic Bayesian network for intention conjecture[4]. The user’s interaction intention with service robots in daily application can be described by a variety of ways, such as the body posture, gestures, voice commands, facial expressions or measurement of physiological information like heart rate, skin reactions, etc. Due to the one-way interaction with human and a lack of communication and learning ability, the robots with traditional interaction mode cannot cooperate well with users. Human intention evaluation acting as a natural interaction method, enables robots to understand human intentions and keep learning. Lots of research achievements have been achieved in human’s coordination with the computer or robot through user’s intention evaluation algorithm. Z. Wang and K Mülling proposed a latent variable model and inferred the intention through the observation of human motion[5]. Graham C. and Juan C. proposed approximate E-M algorithm aimed at the parameters and state assessment of nonlinear stochastic model[6]. C.Morato used the extended Kalman filter and combined with multiple Kinect sensor to evaluate human bone structure[7]. These algorithms did not provide inference about behavioral intention. Wang Lei put forward a secondary assessment method, which was based on the behavior sequence estimation and behavior rules fitting, to facilitate the multi-robots system with human intention recognition[8]. P. A. Lasota put forward a robot speed control algorithm through the research on robot joint angle values and the accurate position of users acquired by the optical camera[9]. Harish chaandar Ravichandar and Ashwin Dani proposed the method of using multi-models to infer the human intention[10]. In this paper, the target position which the user's hand would reach was taken as his intention. For example, when the user moves his arms, he wants the robot to predict his target position and adjust the corresponding action. Aimed at predicting the target position, an intention inference algorithm is 736 Advances in Engineering Research (AER), volume 105 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). ultimately proposed based on a research about the collected human skeleton data and the model built for the human arm movement. The principle of this algorithm is shown in Figure 1. Collecting human skeleton data Data filting Parameter initialization