# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 3 Model Selection and Data Processing
The number of estimators requires one to achieve lower out-of-bag (OOB) error, \(<\)50, for the sampled dataset, as shown in Figure 9. Here the estimator range was from 15 to 175. The error rate reduced below the level 50 estimator. The RSSI value increased as the robot moved towards the transmitter. The higher the RSSI value, the higher the signal strength. The prediction model convergence for all RSSI register values for four random position is shown in Figure 10.  
The gray colored dots are RSSI register values. The random forest classification algorithm tries to converge; it can be seen as red lines. The bar graph of actual and predicted values for 25 random cases is shown in Figure 11.  
Figure 10: Model point convergence. Figure 9: Out-of-bag (OOB) error rate vs. estimator. The robot predicted the position using the real-time scan data and prediction model. We marked the best-predicted position vis-a-vis the other points as an estimated position. The robot then moved to the nearby four points, centering the predicted position in the grid. If for a certain point, a precise prediction was available as per the assumption made by a robot, then before moving, the robot conformed to that point as an accurate estimation. The cumulative distribution of errors (in meters) was determined. In an average-sense, the RF classification method gets its 90% position predictions within the 3 m range. Additionally, the RF-based method gets a maximum error of 6 m for any positions. The RF, KNN, RBF SVM, and MLP achieved 90% accuracy at 2.87, 3.7, 3.85, and 5 m, as shown by vertical dotted lines in Figure 12.  
The median accuracies for this study were 0.030, 0.045, 0.05, and 0.062 m respectively. The threshold point to select the classification method was 3.2 m because the error should not go beyond half of the region of interest area. The threshold point was decided based on the mapping area  
Figure 11: Actual vs. predicted values. Figure 12: Cumulative distribution function of the position error in grid. where the distance between two consecutive points was 1.6 m and was set equal to two grid points, or 3.2 m from prediction point. For RF, RBF SVM, KNN, and MLP respectively, the accuracies of the individual methods at threshold points were 91.7%, 86%, 84.3%, and 82.3%. The accuracy depends on the sampling data and the real-time link quality. The object detection and recognition module and the facial detection and recognition module work independently. Object detection results are in Figure 13 (Part: A). The robot tries to understand the surroundings and identifies the objects and registers their position in the database. The robot detected the objects such as a chair, laptop, and person in the detection area; as the prediction model was unexposed to the training data for the laboratory storage cabinet, some such objects were unrecognizable. A facial detection and recognition model output helps in acquiring inputs from authorized persons. The detected person is recognized through a respective database name, whereas the person who has no record in a database is deemed to be an unknown person shown in Figure 13 (Part: B). The face orientation and lighting conditions may affect the detection process, and so the robot tries to change its orientation for appropriate adjustment to the lighting conditions. In case of detection error, the system can get the target point through manual inputs. The robotic system receives necessary action for a change in target or to scan the area with the help of the proposed localization method. This system is useful for surveillance without human interaction among the set-points. The selection of classification methods depends on many parameters, such as data collection methods, the filtering process, and consideration of the dynamic environment. The proposed robotic navigation system algorithm has liberty to select a suitable classification method based on the dataset.