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.