Behaviour Trees for Evolutionary Robotics

Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework i...

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Bibliographic Details
Published inarXiv.org
Main Authors Scheper, Kirk Y W, Tijmons, Sjoerd, de Visser, Coen C, Guido C H E de Croon
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.08.2015
Subjects
Computer simulation
Evolution
Flapping wings
Flight tests
Intelligibility
Micro air vehicles (MAV)
Neural networks
Robotics
Sensors
Task complexity
Vision systems
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Summary:Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour 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-gram DelFly Explorer flapping wing Micro Air Vehicle equipped with a 4-gram 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 optimised behaviour 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.
ISSN:2331-8422