# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
### A Survey on Simulators for Unmanned Aerial Vehicles  
Over time, different types of simulators have been proposed and used successfully to develop autonomous and non-autonomous tasks with UAVs. The research in [21] proposes a new dynamic model for a conventional fixed-wing airplane that considers four controllable variables. This new model is realistic, and numerical simulations demonstrate its effectiveness. Regarding the work in [22], a linear dynamic model for a fixed-wing aircraft is obtained and validated through numerical simulations. Another research uses the dynamic model obtained by the Newton-Euler method to simulate an unmanned aerial vehicle [23]. The simulations are numerical in the previous work and test different control strategies. A work very similar to the above ones can be found in [24]. In the same way, the aerial vehicle model is obtained and simulated to test control laws. As for the work of [25], Unity and the conservative UAV dynamics (i.e., environmental factors such as frictions are not considered) are used to perform realistic 3D simulations. The proposal is capable of simulating predefined rescue tasks with various vehicles. For its part, the research in [26] seeks to determine, precisely, the dynamic model of an unmanned aerial vehicle by integrating physical components, sensors, control boards, and communications elements. The dynamic model is simulated on an embedded board and enables the development of advanced flight controls and navigation systems. Similar to the previous work, ref. [27] proposes a simulation in an embedded system to aid the tests of non-linear control strategies. The dynamic model parameters are identified from the inputs and outputs of an actual aerial vehicle. Other work that simulates air vehicles in embedded systems can be observed in [28]. In particular, this work considers the operation of commercial hardware devices within the dynamic model. The dynamic model of different ground and air vehicles, and several of their components, is used to perform a Hardware-In-the-Loop (HIL) simulation in an embeddedsystem in [29]. Missions (predefined tasks) can be assigned from the simulation, and the data related to the position and orientation of the vehicles can be acquired. The work in [30] presents another simulator with HIL for unmanned aerial vehicles. This simulator allows testing different control strategies. On the other hand, the work in [31] uses Unity 3D to produce a high-fidelity three-dimensional simulation of a quadrotor and its flight environment. This simulation uses ROS (Robot Operating System) to simulate the behavior of some sensing elements (laser sensors). However, it does not consider a dynamic model or control elements. The work in [32] develops the simulation of UAVs through ROS and a 3D simulation software for robotics called Gazebo. Like the previous work, the dynamic model is not developed explicitly, and the simulation allows sensing elements such as cameras. This simulator is used mainly for route planning. The work in [33] provides a simulator for unmanned aerial vehicles at the network level. This tool simulates communication and interactions between hardware devices commonly used in this type of vehicle. Another work focused on the simulation of communication networks with unmanned aerial vehicles can be found in [34]. A different work, consulted in [35], proposes a simulator for conventional fixed-wing aircraft. This simulator includes the dynamic model of the aircraft and its sensors and provides a means to visualize a 3D simulation. The above is done with commercial software and allows predefined tasks to be defined and simulated. The work in [36] presents an investigation very similar to the previous one but focused on the simulation of multiple aircraft. This simulator also allows testing different control strategies for a variable number of vehicles. An additional example of a UAV simulator in the agriculture context is at [37]. This simulator tests the vehicle group coordination and control strategies for crop monitoring tasks.