# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
###### Abstract  
Unmanned Aerial Vehicles (UAVs) support humans in performing an increasingly varied number of tasks. UAVs need to be remotely operated by a human pilot in many cases. Therefore, pilots require repetitive training to master the UAV movements. Nevertheless, training with an actual UAV involves high costs and risks. Fortunately, simulators are alternatives to face these difficulties. However, existing simulators lack realism, do not present flight information intuitively, and sometimes do not allow natural interaction with the human operator. This work addresses these issues through a framework for building realistic virtual simulators for the human operation of UAVs. First, the UAV is modeled in detail to perform a dynamic simulation in this framework. Then, the information of the above simulation is utilized to manipulate the elements in a virtual 3D operation environment developed in Unity 3D. Therefore, the interaction with the human operator is introduced with a proposed teleoperation algorithm and an input device. Finally, a meta-heuristic optimization procedure provides realism to the simulation. In this procedure, the flight information obtained from an actual UAV is used to optimize the parameters of the teleoperation algorithm. The quadrotor is adopted as the study case to show the proposal's effectiveness.  
virtual reality; teleoperation; simulator; unmanned aerial vehicles; meta-heuristics Article Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation  
[PERSON], [PERSON], [PERSON] [PERSON], [PERSON], [PERSON] [PERSON], Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation  
2021  
15 December 2021  
17 December 2021  
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current challenges in real-time image processing, the human pilot is necessary to perform tasks such as manual obstacle avoidance to guarantee the safe and correct accomplishment of a mission [12]. On the other hand, current technology has allowed cooperation between the human and the computer, allowing the latter to support some tasks that the human performs (or vice versa). In [13], the actions of a human pilot are complemented by data science for better obstacle avoidance and denied airspace with a UAV. In a different case, the computer uses the flight information of a human pilot to teach a UAV to fly in [14].  
So, a human operator can be dispensed under approach (1) when technology and operating environment conditions are safe and allow the UAV to fly autonomously with few restrictions and low risk. Otherwise, the activities of the UAV must be supplemented or carried out under human supervision by approach (2).  
In approach (2), the human is responsible for maneuvering the UAV in the best possible way. Nevertheless, learning to pilot UAVs is a difficult task, and the ability depends on the amount of training the human has previously received [15]. Moreover, training is essential to comply with many of the current regulations on UAVs [16]. As in many other contexts, the human trains by repeating a task several times until he masters it, and the case of flight training is no exception [17]. However, training with this type of system has two main difficulties:  
* Many of the UAVs are expensive equipment, and improper operation can cause damage to the vehicle itself or to its environment, which entails excessive expenses as a consequence [18; 19].
* Currently, efficient energy storage and prolonged storage use are open problems [20]. In addition to generating additional costs, the above limits the possibility of continuous training with UAVs since the batteries on board tend to be small and short duration.  
The two previous difficulties can be solved through the simulation tool, minimizing expenses and training times and reducing the risks and damages of incorrect operations. Next, it is studied how the simulation tool has contributed to the development of UAVs.