Evolved Control of Natural Plants: Crossing the Reality Gap for User-defined Steering of Growth and Motion

Daniel Nicolas Hofstadler, Mostafa Wahby, Mary Katherine Heinrich, Heiko Hamann, Payam Zahadat, Phil Ayres, Thomas Schmickl
ACM Transactions on Autonomous and Adaptive Systems 12(3) (2017)

Abstract:

Mixing societies of natural and artificial systems can provide interesting and potentially fruitful research targets. Here we mix robotic setups and natural plants in order to steer the motion behavior of plants while growing. The robotic setup uses a camera to observe the plant and uses a pair of light sources to trigger phototropic response, steering the plant to user-defined targets. An evolutionary robotic approach is used to design a controller for the setup. Initially, preliminary experiments are performed with a simple predetermined controller and a growing bean plant. The plant behavior in response to the simple controller is captured by image processing and a model of the plant tip dynamics is developed. The model is used in simulation to evolve a robot controller that steers the plant tip such that it follows a number of randomly generated target points. Finally, we test the simulation evolved controller in the real setup controlling a natural bean plant. The results demonstrate a successful crossing of the reality gap in the setup. The success of the approach allows for future extensions to more complex tasks including control of the shape of plants and pattern formation in multiple plant setups.


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