Insects and hummingbirds exhibit extraordinary flight capabilities and can
simultaneously master seemingly conflicting goals: stable hovering and
aggressive maneuvering, unmatched by 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
due to various constraints. Here, we present an open source high fidelity
dynamic simulation for FWMAVs to serve as a testbed for the design,
optimization and flight control of FWMAVs. For simulation validation, we
recreated the hummingbird-scale robot developed in our lab in the simulation.
System identification was performed to obtain the model parameters. The force
generation, open-loop and closed-loop dynamic response between simulated and
experimental flights were compared and validated. 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.