Optimising Robotic Pool-Cleaning with a Genetic Algorithm

Abstract

Demand for Genetic Algorithms (GA) in research and market applications has been increasing considerably. This can be explained through big data and the necessity of interpreting them in an automatic, efficient and intelligent way. In the case of intelligent systems for unmanned vehicles there are two well-defined subsystems: autonomous and robotic navigation. Despite the present day’s good development of the latter, taking the best decisions is an essential robot’s attribute that can only be acquired with the former. This work presents a fully programmed GA for a robot to walk around a planar graph G with the highest efficiency. Each robot’s action is one among five kinds of genes, and fourteen of them build a chromosome, namely a sequence of actions for the robot to walk all around G. The efficiency of a chromosome is given by the number of visited vertices and the amount of saved energy, which are both computed by a fitness function. Our GA returns near-optimal chromosomes for the robot to clean the whole reflection pool with the least energy consumption. Our Coverage Path Planning differs from others in the literature because they consider obtaining a near-optimal sequence of vertices for the robot to follow in that order. Moreover, for such a sequence they do not allow vertex repetitions, whereas in our developed algorithm the robot can pass more than once in a same vertex, with the objective of guaranteeing a lower energy consumption. This objective already makes our best chromosomes avoid repeating vertices, as we have observed in our experiments.