## 5 Results  
Figure 3 illustrates the results obtained in the first experiment, where the prediction models were executed with data from a real environment. It is possible to observe three sections of a route where LI does not deviate from obstacles, a behavior which was already expected. However, ML models were able to move around curves, avoiding obstacles, thus answering \(RQ1\). In other words, what is observed is the ability of ML models to learn where the obstacles are in the environment. This learning takes place without the use of any information from the plan of the environment, using only the coordinates of the tracked objects  
Figure 4 illustrates the variation of \(d=\{x|x\in\mathbb{N},x\leq 30\}\) simulating instances of low and high latency by using a sample of 50,000 records. It is observed that there is a performance loss as \(d\) increases. This is due to the increase in the distance between the interpolated coordinates and consequently the greater difficulty of predicting the trajectory taken. However, even for values of \(d=30\), ML models maintain \(R^{2}>0.8\). This result answers \(RQ2\), showing that it is possible to increase latency up to 30 times while maintaining good predictions. As for the LI method, it is possible to observe that the values of the metrics worsen considerably as the value of \(d\) is increased. This is fundamentally due  
Figure 3: This Figure represents the floor plan of a real environment. The arrow and black X indicate the start and end point of the path, respectively. The red X’s represent the real coordinates. The green circles, gray hexagons, yellow triangles, blue stars, purple diamonds, and orange rotated triangles correspond to the predictions of LI, Histogram-Based Gradient Boost, Ada Boost, ANN, Random Forest, and XGBoost, respectively. to the method of assuming a path in a straight line between the two interpolated points. As \(d\) increases, it is more unlikely that the path taken was in a straight line. Regarding Figure 4, it is observed that the higher the value of \(d\), the higher the MAE and RMSE values. By definition, MAE and RMSE have the same values when the error is uniform for all examples in the test dataset [51]. In cases where the total error accumulates in a few instances of the test dataset, RMSE will increase further [52]. This is due to the RMSE characteristic of being more sensitive to outliers. In the case of Environment 1, it is possible to observe that the peaks of the RMSE values are approximately twice the MAE values, \(\approx\)12 and \(\approx\)24 respectively. The proportional difference is smaller for the case of Environment 2. Figure 4: For each environment, three Figures are shown, each with a different metric (\(Y\) axis) related to the variable \(d\) (\(X\) axis). These metrics are \(R^{2}\), MAE, and RMSE. \(R^{2}\) corresponds to (**A**,**D**,**G**,**J**). MAE corresponds to (**B**,**E**,**H**,**K**). And lastly the RMSE corresponds to (**C**,**F**,**J**,**L**). In each Figure, two scatter plots are displayed, with the same representations on the \(X\) and \(Y\) axes, the difference being only a complete view on the smaller plot and a greater focus on ML algorithms on the larger plot. The green circles, gray hexagons, yellow triangles, blue stars, purple diamonds, and orange rotated triangles match the predictions of LI, Histogram-Based Gradient Boost, Ada Boost, ANN, Random Forest, and XGBoost, respectively. The horizontal bars refer to the standard deviation, being related to each model through color. The values shown are the result of averaging the \(X\), \(Y\), and \(Z\) axes of Environments 1, 2, and 3 and the \(X\) and \(Y\) axes of Environment 4. Environment 2, with \(MAE\approx 28\) and \(RMSE\approx 48\). This greater proportional difference occurs due to the error of the predictions being concentrated in fewer examples in the case of Environment 1.