INTEGRATION OF LOCAL AND GLOBAL SCHEDULER INTO A MOBILE ROBOT CONTROL SYSTEM

Abstract

This paper investigates the problem of integrating local and global motion planning methods
in a robot control system. The current level of technological development allows mobile robots
not only to follow predetermined coordinates, but also to make real-time decisions independently
of the operator, reacting to changes in the environment. However, the dynamic nature of the environment and the constraints on planning time, as well as the high speeds of mobile robots, complicate
the problems solved by planning algorithms. In this paper, some motion planning methods
based on cellular decomposition (such as A*, D* and Wavefront) and random search procedures
on graphs (such as fast growing random RRT trees and probabilistic roadmaps PRM) integrated
with a motion trajectory prediction algorithm (DWA) are reviewed. A study of the performance
characteristics of each of the above algorithms has been conducted, as well as a series of numerical
and in-situ experiments to analyze the effect of map topology on the execution time and
memory usage of the algorithms. The effect of the speed of local and global planning under different
configurations of the external environment was investigated. To confirm the effectiveness of the
investigated algorithms in real conditions, software for a mobile robot based on a wheeled chassis
has been created. The paper presents structural and functional schemes of interaction between the
implemented modules of planning and motion control of the mobile robot and the environment.
It also presents a mathematical model of a wheeled platform, for which, based on the considered
methods, motion planning algorithms are developed. In this paper, quantitative measures including
the computation time of the motion planning algorithm and the amount of memory used by the
algorithms under different environment maps are evaluated. Both environments with randomly
placed obstacles and different types of mazes are considered. The implementation of the developed
algorithms in the ROS-2 environment is also described. It is shown that the implemented system
provides real-time control and motion planning of the mobile robot.