# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 1 Introduction
The authors collected 68,500 samples (53,500 training:15,000 testing) for an area of 512 m\({}^{2}\) (32 m \(\times\) 16 m) with eight wireless nodes. The authors evaluated the performance of RSSI based fingerprint localization with the help of a K-ELM-based positioning system, KNN (k-nearest neighbors), BYS (Bayesian), classic ELM, and OS-ELM (online sequential ELM) algorithms. Among them, the K-LEM algorithm achieved 90% accuracy at 8.125 m compared to other methods. The K-LEM achieved a lower root-mean-square error but it took 60.847 s for testing and 0.018 s for training. The 90% accuracy was achieved at 8.125 m in a relatively higher dimension indoor (not justified obstacles) environment of 32 m \(\times\) 16 m using the K-LEM classification method to navigate the robot [18]. [PERSON] et al. have commented on the ISO/IEC 18305:2016 standard which states that three different kinds of ELTs (entry to be localized/tracked) are: person, robot, and object. This standard also suggests that the test should be performed separately for each ELTS. There is no scenario defined for the robotic system. Additionally, the number of tests required is higher for a universal system. As per the standard, the number of points per floor is in the range of 50 to 200. The accuracy of a robotic system should not go beyond 4 m or not larger than half the average size of the sample area of the building [19]. In Microsoft indoor localization competition, developers made a position estimation for 300 m\({}^{2}\) area using a neural network or the \(K\) nearest classification-based method. The competition concluded that the surrounding area affects the prediction model. Other sampling techniques may improve such results [20]. In [21], [PERSON] et al. stated that only RSSI signal improvement may not lead to position accuracy. [PERSON] et al. proposed the media access control address also plays a vital role to achieve higher accuracy in multi-story buildings. Based on different MAC addresses received on the different floor, they identified the floor number and implemented a further localization method for mapping. The authors achieved1.8 m accuracy horizontally with the floor number where the receiver was. In the emerging field of indoor localization techniques, ultra-wide band (UWB) is a newer technology [22]. For results required from a large number of features and searches in a random section, the best way is to split each node [23]. Random forest-based forecasting converges more possible inputs than linear regression, multiple linear regression, and CNN networks [15]. The estimator calculations for random forest classification model concerning their features provide a better estimation. If the number of estimators is calculated, the prediction can be faster for real-time data [24]. Computer vision-based indoor navigation uses the marker-based approach. Based on the size, shape, distance from the camera, focusing methods, and different light conditions, the marker-based indoor navigation can be used for indoor navigation [25]. A robotic system should be capable of navigating in an indoor environment and have capabilities to understand the surroundings. In the visual localization and mapping method, the system builds the three-dimensional point cloud model for position estimation and localization. For data storage and comparison with real-time data, one needs significant processing capabilities. Visual localization and mapping have challenges such as the dependency on illumination conditions, and a high processing time for a dense and complex environment. To process and analyze real-time data, the graphic processor provides a higher processing speed. For a system with a lower processing capability, it is hard to achieve real-time processing speed with object detection simultaneously. The errors in depth measurements are directly related to the distance between the object and the sensor. If the distance increases, the error also increases. Additionally, the data quality depends upon the sensor depth resolution [26].