Hierarchical model simulation based on EKF for mobile robots

Abstract

Abstract With the technological advance grows the need for optimization in the tasks performed by mobile robots, these must be endowed with autonomy and other decisions, this is because the operating environments become more complex and with obstacles and objectives that change position, adding to this the interferences of the workspace such as disturbances in the environment and other system noises. To address this problem, the use of navigation algorithms, route creation, position estimation, path tracking, object recognition and others are used. In the present study, a spiral methodology was used with which a mathematical model based on the extended Kalman filter (EKF) was developed for trajectory tracking and position estimation of a differential robot using its kinematics. For the validation of the mathematical model Matlab was used together with CoppeliaSim where the different test scenarios were carried out, later the results of EKF are compared with those of Kevin Passino’s algorithm, finding superior in most aspects to the EKF except in the time when Kevin Passino’s algorithm achieves a shorter simulation time.