Automatic Synthesis Gait Scenarios for Reconfigurable Modular Robots Walking Platform Configuration

Abstract

Reconfigurable mechatronic modular robots distinguished mainly by their ability to adapt their structure to specific tasks to be performed as well as to specific environments, are of great interest for a wide range of different applications. One of the key problems in motion control of this type of robots lies in the necessity to use original algorithms for each of the possible configurations whose variety is determined by the structure of mechatronic modules their number and the coupling option selected. Some standard configurations of mechatronic modular robots allow the possibility to develop motion control algorithms invariant to the number of modules in the kinematic structure. Yet, a promising approach to solving the problem is generally related to the development of self-learning means and methods to enable an automated synthesis of motion control algorithms for multi-unit mechatronic modular robots, taking into account the selected configuration. The present article discusses the results of exploratory research on using the apparatus of self-learning finite state machines for solving the problem of automated synthesis of gait scenarios in the walking plat-form configuration. The paper presents the results of model experiments confirming the workability and efficiency of the developed algorithms.