Prediction of Component Failures of Telepresence Robot with Temporal Data
Jayesh Soni, Nagarajan Prabakar, Jong-Hoon Kim(1)
School of Computing and Information Sciences Florida International University, Miami, FL 33199
(1) Department of Computer Science, Kent State University, Kent, OH 44242
jsoni@fiu.edu, prabakar@fiu.edu, jkim@cs.kent.edu
ABSTRACT With recent advances in computer and sensor technologies in the last few decades, the use of robots for various applications has increased enormously. The reliability of robots depends on the minimization of component failures and downtime. To improve the reliability, periodic monitoring of components and their behavior are essential to inference component fatigue and potential breakdowns. Since fully autonomous robots are very expensive, telepresence robots are affordable for mass scale deployment and can be controlled by a trained human operator like avatars. To increase the efficiency and to reduce the downtime of telepresence robot service, it is essential to observe the various commands performed on the robot and to analyze the samples of component status over a long period. We propose an efficient data driven model with a collection of frequent time-stamped data from various components of a telepresence robot and predict potential failure warnings. The collected historical datasets are analyzed to determine an accurate machine learning model for increased failure prediction of components. Analysis of this large collection of data will be performed on a cloud computing platform to alleviate the computational load on telepresence robots. With the incoming temporal data, this machine learning model predicts the component status and probability of failure in real-time. Potential Applications of the proposed approach also includes detection of component malfunction, estimating the degree of movement of various components for satisfactory level of performance, and migration of workload among multiple telepresence robots in a team work environment.