Sensor fusion system using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it i s very important to estimate the state of systems. We must measure the phen omena of systems by multiple sensors and estimate the state of systems by a cquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor infor mation and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with r ecurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Rad ial Basis Function (RBF) with insensitive ranges. The shape of the membersh ip functions can be adjusted by a learning algorithm. The learning algorith m is based on the steepest descent method and incremental learning which ca n add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.