Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Name : 35th Fuzzy System Symposium
Number : 35
Location : [in Japanese]
Date : August 29, 2019 - August 31, 2019
The purpose of this research is to study an algorithm to estimate the state more accurately from the information of the 6-axis sensor mounted on the Mini 4WD. The six-axis sensor is a sensor that measures six variables of acceleration of three axes and angular velocity around three axes by MEMS. By performing these integrations, it is possible to estimate the position, velocity and direction angle by inertial navigation calculation. However, due to the inclusion of second-order integrals and the inevitable stationary noise contained in these sensors, divergences and large errors in the estimates are inevitable. For this reason, in this research, we make use of the fact that the Mini 4WD race is performed with a combination of prescribed course parts, and attempt to raise the accuracy of state estimation with this as a standard by creating a course model. For noise-based observation systems, there are Kalman filters and particle filters, but it is difficult to apply directly to the course-constrained mini 4WD system model because it contains nonlinear and piecewise non-differentiable motions. Therefore, we assumed the nonlinear course model to be position-dependent movement switching, performed model calculation, and attempted correction by integrating the results.
