Maze Exploration Behaviors Using an Integrated Evolutionary Robotics Environment

Abstract

This paper presents results generated with a new evolutionary robotics (ER) simulation environment and its complementary real mobile robot colony research test-bed. Neural controllers producing mobile robot maze searching and exploration behaviors using binary tactile sensors as inputs were evolved in a simulated environment and subsequently transferred to and tested on real robots in a physical environment. There has been a considerable amount of proof-of-concept and demonstration research done in the field of ER control in recent years, most of which has focused on elementary behaviors such as object avoidance and homing. Artificial neural networks (ANN) are the most commonly used evolvable controller paradigm found in current ER literature. Much of the research reported to date has been restricted to the implementation of very simple behaviors using small ANN controllers. In order to move beyond the proof-of-concept stage our ER research was designed to train larger more complicated ANN controllers, and to implement those controllers on real robots quickly and efficiently. To achieve this a physical robot test-bed that includes a colony of eight real robots with advanced computing and communication abilities was designed and built. The real robot platform has been coupled to a simulation environment that facilitates the direct wireless transfer of evolved neural controllers from simulation to real robots (and vice versa). We believe that it is the simultaneous development of ER computing systems in both the simulated and the physical worlds that will produce advances in mobile robot colony research. Our simulation and training environment development focuses on the definition and training of our new class of ANNs, networks that include multiple hidden layers, and time-delayed and recurrent connections. Our physical mobile robot design focuses on maximizing computing and communications power while minimizing robot size, weight, and energy usage. The simulation and ANN-evolution environment was developed using MATLAB. To allow for efficient control software portability our physical evolutionary robots (EvBots) are equipped with a PC-104-based computer running a custom distribution of Linux and connected to the Internet via a wireless network connection. In addition to other high-level computing applications, the mobile robots run a condensed version of MATLAB, enabling ANN controllers evolved in simulation to be transferred directly onto physical robots without any alteration to the code. This is the first paper in a series to be published cataloging our results in this field.