# Autonomous Environment Generator for UAV-Based Simulation
## 2 Related Works  
For related works, other projects involving simulator-based testbeds that allow for environmental models will be directly compared with our project. While several projects exist in such a context, there are many significant differences that need to be addressed. These related testbeds include:  
* _OpenUAV_: This testbed allows for testing of \"swarms\" of UAVs using integration with the Gazebo simulation system. OpenUAV also supports a containerized desktop environment and mission planning software [2]. OpenUAV also features remote desktop accessibility. However, the OpenUAV simulation environment lacks a level of realism, due to relatively-empty and generic environments. Our project's software allows for generation of real-life, specific locations with auto-placed and scaled models.
* _Autonomous Airship Sim_: This platform also combines the testing of UAVs with the Gazebo simulation system [3]. However, this project focused on the benefits and powerful capabilities of Gazebo and lacks integration with broader UAV applications, like drones, due to a concentration on airship testing. There is also no integration of any image processing abilities, as the airships would be used independently of collected satellite data. Very little was shown about the Gazebo environment themselves, as well. Our project allows for more dynamic environments, as well as simulation support, for a wide variety of UAVs.
* _AirSim_: This platform from Microsoft allows for a wide variety of UAV testing using the Unreal Engine. AirSim also supports the integration of other machine learning frameworks, combined with high-rendered environments [4]. However, the biggest drawback is the environment creation itself. Not only are AirSim's environments not based on real locations, but creating custom ones involves directly editing several Unreal Engine files and restarting your computer. Our platform solves this issue by making custom environment generation intuitive for users with a single click. Due to our machine learning integration, users will not have to edit any files to create a unique simulation of a real-life location.
* _UAS Collision Avoidance Sim_: This platform utilizes the FlightGear simulation program to test aircraft collision with a virtual control station [5]. While this project had certain success comparing simulated collisions, this platform's testing environment lacks several features. Although there appears to be some satellite image integration in the FlightGear sim, the entire world is 2D, with no spawned 3D models. This lack of realism, combined with a lack of machine learning integration, leaves more to be desired.
* _Lincoln Lab Sim_: This UAV simulation was developed by the Massachusetts Institute of Technology, and includes realistic, 3D environments. The Lincoln Lab sim can also test a large variety of UAVs and collect varying sensor data. This sim also features control and collision avoidance algorithm testing [6]. However, the largest problem with this design is the requirement of using their specified 17,000 square-foot indoor testing facility. A drone must physically be flying within this facility, and then cameras and detectors can show the drone in the simulation. This drawback is very limiting for practical future testing.
* _Vision-Based UAV Platform_: This platform tests UAVs using the Gazebo simulator, specifically to test the vision capabilities of such UAVs [7]. However, once again, a problem arises due to less-than-realistic Gazebo simulations. The environments used in these simulations have generic models that must be manually placed and scaled.  
This Vision-Based UAV Platform also admittedly has a limited variety of testable UAV models, making this platform less than ideal.