Genetic programming, logic design and case-based reasoning for obstacle avoidance

Abstract

This paper draws on three different sets of ideas from computer science to develop a self-learning system capable of delivering an obstacle avoidance decision tree for simple mobile robots. All three topic areas have received considerable attention in the literature but their combination in the fashion reported here is new. This work is part of a wider initiative on problems where human reasoning is currently the most commonly used form of control. Typical examples are in sense and avoid studies for vehicles – for example the current lack of regulator approved sense and avoid systems is a key road-block to the wider deployment of uninhabited aerial vehicles (UAVs) in civil airspaces. The paper shows that by using well established ideas from logic circuit design (the “espresso” algorithm) to influence genetic programming (GP), it is possible to evolve well-structured case-based reasoning (CBR) decision trees that can be used to control a mobile robot. The enhanced search works faster than a standard GP search while also providing improvements in best and average results. The resulting programs are non-intuitive yet solve difficult obstacle avoidance and exploration tasks using a parsimonious and unambiguous set of rules. They are based on studying sensor inputs to decide on simple robot movement control over a set of random maze navigation problems.