Application Model of Multi-Sensor Data Fusion in Mechatronics Equipment

This paper discusses the application model of multi-sensor data fusion technology in mechatronics equipment. Firstly, this paper summarizes the importance of mechatronics equipment in modern industry, military and medical fields, and emphasizes the limitations of single sensor in complex environment, highlighting the necessity of multi-sensor data fusion technology. Then, the paper introduces three levels of multi-sensor data fusion in detail: data level fusion, feature level fusion and decision level fusion, and discusses their applications in different fields, such as industrial production lines, intelligent robots and military fields. This paper puts forward an application model framework that includes five modules: data acquisition, preprocessing, fusion, decision-making and execution control, and expounds the functions and workflow of each module in detail. In terms of data fusion methods, this paper discusses the commonly used technologies such as Kalman filter, particle filter and nonlinear optimization, and analyzes their applicability in different system characteristics and application scenarios. Through simulation analysis, this paper verifies the application of multi-sensor data fusion algorithm in autonomous mobile assembly robot, and shows the potential of the fused data in improving the accuracy and stability of robot autonomous movement and assembly. Finally, the paper points out the challenges faced by multi-sensor data fusion technology, and looks forward to its development trend in the future electromechanical integration equipment, including miniaturization, networking and wireless. The efficient multi-sensor data fusion model constructed in this paper provides a solution to the difficult problems in sensing, decision-making and execution of mechatronics equipment, and at the same time provides new ideas for researchers in related fields and promotes technological progress.