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Insects and hummingbirds exhibit extraordinary flight performance and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, which are unmatched by conventional small scale man-made vehicles. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap. However, design and control of such systems remain challenging. Here, we present an open source high fidelity dynamic simulation for FWMAVs. The simulator serves as a testbed for the design, optimization and flight control of FWMAVs. To validate the simulation, we recreated the at-scale hummingbird robot developed in our lab in the simulation. System identification was performed to obtain the model parameters. Force generation and dynamic response of open-loop and closed loop systems between simulated and experimental flights were compared. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible with OpenAI Gym environment. As a benchmark study, we present a linear controller for hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real transfer of both control policies onto the physical robot, further demonstrating the fidelity of the simulation.

Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals