A fuzzy-genetic based embedded-agent approach to learning and control in agricultural autonomous vehicles

This paper describes the design of a fuzzy controlled autonomous robot, incorporating genetic algorithms (GA) based rule learning, for use in an outdoor agricultural environment for path and edge following activities which involve spraying insecticide, distributing fertilisers, ploughing, harvesting, etc. The robot has to navigate under different ground and weather conditions. This paper addresses the development of an online self-learning system based on modified version of the fuzzy classifier system. The proposed technique has resulted in rapid convergence suitable for learning individual behaviours online without the need for simulation. The controller was tested on both an in-door and out-door mobile robot operating with different types of sensors, propulsion and steering. Experiments include operating the vehicle following irregular crop edges under different weather and ground conditions within a tolerance in the order of 2 inches