## 1 Introduction  
In the past several decades, robotic systems have played an important role in artificial intelligence (AI), allowing solutions to existing problems that reduce the necessary resources. AI is the field of science that helps machines improve their functions, in the areas of logic, reasoning, planning, learning, and perception [1]. These features bring efficient performance to the systems in different fields. One of the major topics in the AI field is the development of autonomous machines, such as robotic systems [2]. A robotic system is a reprogrammable multifunctional manipulator, designed to move materials, parts, tools, or specialized devices through various programmed movements to perform different tasks [3]. According to the classification of robotic systems, the autonomous level is the most advanced mode [4]. The field of robotic systems has seen great success in many problems by minimizing the resource requirements during online execution when moving between two points. Some of the contributions are surgical robotics [5], mobile robotics [6], hybrid locomotion [7], and bio-inspired robots [8, 9]. According to the calculus of variations [10], a path is defined as the sum of the distances between two consecutive points defined by Equations (1) and (2). For this reason, a path is composed of a set of lines in space. Likewise, one of the principal features to describe a path is the level of safety with respect to avoiding obstacles. Therefore, path planning is the shortest distance between \(m\) obstacles \(O\) and the best value with the highest level of safety in a sequence of points \(p\) of length \(n\). After adding a negative sign to the value, the maximum optimization safety problem is transformed into the minimum optimization problem, as defined in Equation (3) [11]. \[distance(p_{i},p_{i+1})=\sqrt{(x_{i}-x_{i+1})^{2}+(y_{i}-y_{i+1})^{2}} \tag{1}\]  
\[length(p)=\sum_{i=0}^{n}distance(p_{i},p_{i+1}) \tag{2}\]  
\[safety(p)=-\min\min\{min\{min\{Distance(p_{i}p_{i+1},O_{j})\}}\} \tag{3}\]  
Consequently, the path-planning problem requires coordination between the environment and the agent. Hence, it is essential to define rules for the transition between states to achieve an objective. The path-planning problem mainly has two modules based on the definition of AI. The first is the planner, while the second module is the perception of the environment; this is shown in Figure 1[12]. The problem is complex due to the robotic system's characteristics, such as the battery, dimensions, and technologies to perceive the environment, including obstacles, illumination, and other agents in motion. This architecture is efficient for robotic systems whose dimensions and battery are large, such as ground vehicles. In recent years, there has been a growing trend based on unmanned vehicle systems [13], for example micro aerial vehicles (MAVs), but these kinds of systems have reduced dimensions and a limited battery and are used in indoor environments. Subsequently, this architecture is unfeasible and inefficient. However, AI helps machines adopt new features to improve their limited performance. Recent machine learning trends have addressed improving the weaknesses, such as some of the perception solutions with exciting applications in ground vehicles. Some contributions are the analysis of the road using RGB-D sensors [14], detecting and tracking to avoid collisions using RGB-D sensors [15], and extracting the features of the road map to generate trajectories through a LiDAR sensor [16], as well as developments in implementing different techniques to obtain external information [17]. Therefore, data have an essential role in the updated functionalities of the systems, which requires approaches to process the data, such as machine learning. Machine-learning algorithms allow reducing the processing and resources needed, such as the end-to-end approach, allowing the collection of a dataset generated by physical sensors.