Modeling the behavior of a mobile robot using genetic algorithms in harsh ecological environment

Abstract

The article is devoted to the analysis of the behavior of a mobile robot using finite state machine algorithms in order to find a path to a goal and avoid obstacles. After justifying the use of such methods, a standard deterministic finite state machine is analyzed. Next, a theory of Markov chain is applied to this algorithm, as a result of which the finite state machine becomes part of the hidden Markov model. This makes it possible to apply probabilistic methods to planning the robot's behavior. This probabilistic nature of behavior is most promising in harsh ecological environment with unpredictable configurations of obstacles. To compare the efficiency of deterministic and probabilistic finite state machines, we applied a genetic algorithm. In the numerical experiment that we conducted in the Scilab environment, we considered two main types of environments in which a mobile robot can move, in harsh ecological environment, in particular. For each type of environment, we alternately applied each of the specified behavior algorithms. For the genetic algorithm, we used one hundred individuals, which were trained over 1000 generations to find the most optimal path in the complicated environments. As a result, it was found that the deterministic finite state machine algorithm is most promising for movement in the harsh ecological environment, and the probabilistic finite state machine algorithm gives the best result in a complex environment.