Abstract
Abstract Kinematic model is the basic aspect in robot design and motion planning. Kinematic models are idealized, however there exist certain specific aspects of particular robot or environment, so that during navigation, the robot can significantly deviate from the planned trajectory. To increase the accuracy of motions, kinematic model can be improved and to achieve that the artificial intelligence methods can be used. In case of fixed base robots different approaches are used to train kinematics, at the same time, for the mobile base robots it proves to be a more complicated task. The reason is that a mobile robot can move unbound with respect to environment thus it is difficult to control the platform without deviation from the target position, which leads to inaccuracy in the position estimate. This paper presents the method meant for improvement of the accuracy of motion of differential drive platform. Genetic programming is used to obtain the wheel velocity function, from which the coefficient, which describes different factor influence on motion, is obtained. As a result, the kinematic model of a particular platform for a particular task is obtained. This method is effective because the developed kinematic model is more specific than the general one.
Highlights
Robot kinematics deals with the analytical description of displacement, speed, velocity, and acceleration without giving regard to the force that causes the motion, being a fundamental aspect of robot design, analysis, control, and simulation
Variable, dynamic, or nonlinear decision making
Our aim is to provide the solution of how to apply learning in order to improve the kinematic model of a particular mobile robot
Summary
Robot kinematics deals with the analytical description of displacement, speed, velocity, and acceleration without giving regard to the force that causes the motion, being a fundamental aspect of robot design, analysis, control, and simulation. Kinematic models are commonly used to control motion and predict behavior of a robot. Improvement of these models can reduce localization errors, ensure repeatability of motion, and help to avoid obstacles, etc. To improve these models artificial intelligence methods can be applied. It deals with imprecise, variable, dynamic, or nonlinear decision making. A branch of artificial intelligence research is machine learning, which focuses on automatic recognition of complex patterns and decision-making based on sensor data. The degree of autonomy can be increased
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