Towards Behavior Control for Evolutionary Robot Based on RL with ENN

Abstract

This paper proposes a behavior-switching control strategy of an evolutionary robot based on Artificial Neural Network (ANN) and genetic algorithm (GA). This method is able not only to construct the reinforcement learning models for autonomous robots and evolutionary robot modules that control behaviors and reinforcement learning environments, and but also to perform the behavior-switching control and obstacle avoidance of an evolutionary robot in the unpredictable environments with the static and moving obstacles by combining ANN and GA. The experimental results have demonstrated that our method can perform the decision-making strategy and parameter setup optimization of ANN and GA by learning and can effectively escape from a trap of local minima, avoid motion deadlock status of humanoid soccer robotic agents, and reduce the oscillation of the planned trajectory among the multiple obstacles by crossover and mutation. We have successfully applied some results of the proposed algorithm to our simulation humanoid robotic soccer team CIT3D which won the 1st prize of RoboCup Championship and ChinaOpen2010 and the 2nd place of the official RoboCup World Championship on 5-11, July 2011 in Istanbul, Turkey. In comparison with the conventional behavior network and the adaptive behavior method, our algorithm simplified the genetic encoding complexity, improved the convergence rate ρ and the network performance.