Evolutionary robot controllers with competitive and cooperative neural networks

Abstract

This article describes a new approach for control systems for an autonomous mobile robot by using sandwiches of two different types of neural network. One is a neural network with competition and cooperation, and is used for recognizing sensor information where synaptic couplings are fixed. The second is a neural network with adaptive synaptic couplings corresponding to a genotype in a creature, and used for self-learning for the wheel controls. In a computer simulation model, we were successful in obtaining four types of robot with good performance when going along a wall. The model also showed robustness in a real environment.

Towards Behavior Control for Evolutionary Robot Based on RL with ENN

“…Neural networks are very suiable for training with evolutionary computation-based methods which can be represented by a concise set of tunable parameters [32]. Of varieties of neural network structures, the most commonly used structures are layered feedforward and/or recurrent network architectures.…”

“…Neural networks are very suiable for training with evolutionary computation-based methods which can be represented by a concise set of tunable parameters [32].…”

“…Many works have proposed evolutionary robotics control systems using evolutionary adaptations of artificial neural network [26][8][22][1], genetic programming [34][15], and a learning classifier [5]. We commonly use artificial neural networks to evolve a robot controller [11][31] and apply an evolutionary algorithm to design and/or train NN for solving a given task. Many attempts for robot control and an evolutionary algorithm have focused on developing autonomous robots inspired by animals and humans that have robust adaptation and stable behavior in unpredictable environments [17][35].…”

“…The process that the robot activates some basic behavior at some moment is called behavior switching. The robot's behavior and its behavior switching are controlled by using FFNN (Feed Forward Neural Network) and PNN (Probabilistic Neural Network) [10][32][6][13][23]. ANN-based controllers consist of many basic network modules.…”