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.

翻译：开发基于学习的空中机器人导航方法是一个密集的数据驱动过程，需要高度并行的仿真环境。此类仿真器的充分利用受限于缺乏模拟真实机器人接口的并行化高级控制方法。针对这一需求，我们开发了Aerial Gym仿真器，该仿真器能够并行模拟数百万个多旋翼飞行器，并采用针对特殊欧几里得群SE(3)的非线性几何控制器实现姿态、速度和位置跟踪。我们还开发了管理环境中大量障碍物的功能，支持快速随机化以学习导航任务。此外，我们提供了包含模拟摄像头的示例环境，这些摄像头能够在障碍物丰富的环境中捕捉RGB、深度、分割和光流数据。该仿真器是迈向开发当前缺失的、大规模并行化且配备几何控制器的空中机器人仿真的重要一步，同时为导航任务提供了可定制的障碍物随机化功能。我们提供了兼容的强化学习框架训练脚本，用于基于姿态和速度命令接口将机器人导航至目标设定点。最后，我们将仿真器开源，并计划进一步开发，利用替代的基于内核框架加速渲染，以实现深度图像的并行光线投射，从而支持更大规模的机器人数量。