Grammatical Evolution for Optimising Drone Behaviors

Abstract

This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors.