@article{8a89b8035e7640cd8c22b9b9c10e46dc,
title = "Behavior Trees for Evolutionary Robotics",
abstract = "Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimized behavior in simulation is 88%, and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.",
keywords = "Behaviour Tree, Evolutionary Robotics, Reality Gap, MAVs",
author = "Kirk Scheper and Sjoerd Tijmons and {de Visser}, Coen and {de Croon}, Guido",
year = "2016",
month = feb,
day = "17",
doi = "10.1162/ARTL_a_00192",
language = "English",
volume = "22",
pages = "23--48",
journal = "Artificial Life",
issn = "1064-5462",
publisher = "MIT Press Journals",
number = "1",
}