The Performance Evaluation to a Smart Robots Embedded with Machine Learning Schemes

In the article, an algorithm with GMM-UBM (Gaussian mixture model - universal background model) machine learning framework adopted as the cognitive center implemented in a Smart Robot is demonstrated. Moreover, the developed robot is except designed with some embedded smart sensors, the robot is embedded with machine learning technology for applying to the agriculture research field. Specifically, the presented robot could help to analyze the environmental conditions for different plants, e.g. the estimation to weather and humidity, and protection plant from disease destroy. The GMM-UBM algorithm deployed in the smart robot is mainly to control the assignments' behavior precisely. There three of the smart robot are combining with AI (Artificial intelligence) techniques consists of the following equipments: 1) a control movement subsystem, 2) a sensor control subsystem, and 3) an analysis subsystem. The results from the simulation determined the condition of the message options with tag sensing techniques. Moreover, the results have validated that the illustrated system can obtain significantly processing efficiency. Furthermore, the analytic data comes from the analysis subsystem is able to predate the path for the robot move corresponding to the specified conditions.