## 2 Related Works  
It is well known that robotic systems can move between two points, and the path-planning problem is composed of two principal modules based on the AI definition: perception and reasoning. Despite being two decoupled modules, both depend on each other. According to the direct and indirect state-of-the-art approaches, path-planning classification results in a direct and indirect point of reference for the robotic system in space, as shown in Figure 2.  
Figure 2: Direct and indirect approaches for the path-planning problem. The direct approach uses the robotic system as the point of reference to determine the movements. The kinematic movements of the robotic system describe the form of interacting with the environment. Various contributions have used a direct perspective approach, such as dynamic programming [29], soft robotics designing [30], and differential systems [31]. Secondly, the indirect perspective uses the environment as the principal element to capture the robotic system's conditions. Thus, the sensing of the environment is a high priority, with relevant contributions using sampling-based algorithms [32], node-based algorithms [33], and bio-inspired algorithms [34]. Due to the features of MAVs, such as a flight time of approximately 15 min, small dimensions, and sensors such as a conventional camera and an IMU composed of two types of sensors of movement, a gyroscope and accelerometer, they present an optimization problem. Therefore, one of the main challenges in robotic systems is the optimization of the resources needed. For example, some works focused on generating solutions to optimize the flight time and navigation control to make the use of the systems' resources more efficient [35; 36; 37]. This proposal implements an indirect approach using a virtual simulator because the analysis of the environment is of vital importance in executing an action. The virtual simulator has different elements to scan the environment, such as LiDAR, RGB-D, infrared sensors, ultrasonic sensors, radars, and 4D radars. The AirSim simulator [38] shows acceptable behavior to simulate a quadrotor. Moreover, this simulator allows the design of our 3D environment with different data inputs, such as the conventional input, the depth image, and the semantic image. Secondly, a conventional algorithm for path planning was employed to associate a path for each sample generated by the virtual simulation. The \(A^{*}\) algorithm finds short paths using graphs. A graph is a set of nodes with connections between them. Each node is a tuple indicating the destination node and the weight, which describes the connection. One particular characteristic of the algorithm is the movement, highlighting the diagonal between two points. The different movements are based on 45\({}^{\circ}\), forming a star. The heuristics define the complexity of the \(A^{*}\) algorithm, which executes in polynomial time with the implementation of the following expression in Equation (4) [39]. \[\left|h(x)-h^{\prime}(x)\right|=O\big{(}log\big{(}h^{\prime}(x)\big{)}\big{)} \tag{4}\]  
Despite the \(A^{*}\) algorithm's advantages for path planning, it has inadequate performance when the nodes' number increases. Therefore, we used a navigation mesh to reduce the number of nodes. A map in a virtual world is composed of triangles, and each triangle forms a primitive geometry. Thus, the centroid of each primitive geometry-shaped mesh is used, as shown in Figure [40]. AI represents the principles of the biological neuron, employing additive and multiplicative models composed of weights and biases. Simultaneously, an activation function completes the information flow step, generating a learning curve from an objective function. Therefore, it enables finding solutions to complex optimization problems by minimizing the cost of the objective function [41]. Deep learning is a technique that reduces the number of operations for considerable inputs with big dimensions, such as images [42].