Learning User Intention in Networked Mobile Robot Control

Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.

Learning User Intention in Networked Mobile Robot Control

Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.