  Developing learning-based methods for navigation of aerial robots is an
intensive data-driven process that requires highly parallelized simulation. The
full utilization of such simulators is hindered by the lack of parallelized
high-level control methods that imitate the real-world robot interface.
Responding to this need, we develop the Aerial Gym simulator that can simulate
millions of multirotor vehicles parallelly with nonlinear geometric controllers
for the Special Euclidean Group SE(3) for attitude, velocity and position
tracking. We also develop functionalities for managing a large number of
obstacles in the environment, enabling rapid randomization for learning of
navigation tasks. In addition, we also provide sample environments having
robots with simulated cameras capable of capturing RGB, depth, segmentation and
optical flow data in obstacle-rich environments. This simulator is a step
towards developing a - currently missing - highly parallelized aerial robot
simulation with geometric controllers at a large scale, while also providing a
customizable obstacle randomization functionality for navigation tasks. We
provide training scripts with compatible reinforcement learning frameworks to
navigate the robot to a goal setpoint based on attitude and velocity command
interfaces. Finally, we open source the simulator and aim to develop it further
to speed up rendering using alternate kernel-based frameworks in order to
parallelize ray-casting for depth images thus supporting a larger number of
robots.
