Intelligent task planner for cloud robotics using level of attention empowered with fuzzy system

Abstract

Cloud robotics is a field of robotics that endeavors to summon cloud advances, for instance, cloud computing, and other internet advances focused on the advantages of merged framework and shared administrations for robotics. In this research, an intelligent task planning mechanism is proposed to attain a certain level of attention for knowledge sharing among cloud-based robots using mamdani fuzzy inference system (MFIS). The proposed task planning mechanism using mamdani fuzzy inference system (TPM-MFIS), can classify the different stages of attention such as very high, high, medium, low, very low and none. In this recommended intelligent system, cloud robotics services are used to maintain the workload of each robot. Whereas, all fuzzification and decision-making policies handled on cloud to avoid the redundancy task planning consumption on individual level. This system has two input variables that are facial expression and motion rate. Input variables detect the output level of attention to be very high, high, medium, low, very low, and none. This paper presents an analysis of the result's accuracy using proposed TPM-MFIS to model the task planning process.