Evolving Behaviour Trees to Control a Swarm of Flapping-Wing Micro Aerial Vehicles for Greenhouse Exploration
L.H. Uptmoor (TU Delft - Aerospace Engineering)
S. Stroobants – Mentor (TU Delft - Control & Simulation)
M. Popovic – Mentor (TU Delft - Control & Simulation)
G.C.H.E. de Croon – Mentor (TU Delft - Control & Simulation)
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Abstract
Micro aerial vehicles have shown promising use to further automate food production in greenhouses recently. Compared to conventional multirotor drones, flapping-wing drones offer safe and robust operation around plants due to their soft, slowly-moving wings. Their limited sensing and computational capabilities, however, prohibit the use of map-based navigation methods. To compensate for individual shortcomings, swarming ensures scalability and redundancy. This work proposes a hardware setup combining time-of-flight (ToF) and ultra-wideband (UWB) sensing and explores the artificial evolution of behaviour trees as a reactive planning strategy. Genetic programming, paired with CMAES fine-tuning was able to improve a human-designed exploration strategy by 50%. Neuroevolution has been investigated to encourage emergent swarming behaviours, but requires further experimentation in combination with behaviour trees. The solution obtained in simulation can be readily ported to hardware, but a reality gap in performance persists. These findings contribute to the development of lightweight, scalable aerial systems for autonomous greenhouse monitoring.