A Fuzzy Genetic Based Embedded-Agent Approach to Learning & 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 processes which involves spraying insecticide, distributing fertilisers, ploughing, harvesting, etc. The robot has to navigate under different ground and weather conditions. This results in complex problems of identification, monitoring and control. This paper addresses the development of an online self-learning system based on modified version of the Fuzzy Classifier system (FCS). 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 (including a novel wands), propulsion and steering. Experiments include operating the vehicle following irregular crop edges (full of gaps) under different weather and ground conditions within a tolerance of