# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 1 Introduction
The simultaneous localization and mapping (SLAM)-based indoor positioning was tested using Microsoft 3D Kinect Sensor inside the laboratory, and the results are shown in Figure 1. This sensor framework uses a 3D depth sensor which emits infrared pulses and measures the reflected pulse time. Cloud points in \(x,y\) and \(z\) co-ordinates are the sensor outputs. The sensor captures the frames with cloud points at the time of mapping and compares these points in real-time localization. Based on the cloud point matching ratio, the sensor tries to localize itself in a real-time environment. The blue track lines represent the sensor movement trajectory with orientation and sampling position, and the green dots are the perfect match that points to the database value during the real-time scan. Yellow dots indicate the change in the environment after scanning, or are signified as unmatched points. Also, the scanning time required to capture the area depends on the sensors and the processing unit. Sudden movements can affect the mapping and localization results. Figure 1: SLAM-based mapping and localization. To scan an area of 77.51 sq.ft., the sampled database size is greater than 600 MB and this sensor requires high processing power capability, and so we conclude that the system has an advantage in accuracy, but is not suitable for applications which run on limited hardware specifications. Additionally, this sensor will not reliably work in highly illuminated regions, such as window and doors where sunlight is prominently present, thereby affecting the result. The neural network should be fast at processing the live video stream. The YOLO (you look only once) model for real-time object detection process 45 frames per second (fps), whereas fast-YOLO processes 55 fps in real-time. By reducing the frame size, real-time detection is possible with minimal object localization error [27]. An individual facial detection and object recognition model was deployed on a system-specific low power controlling device [28]. In this cited article, the continuous data frames were captured by the camera unit and synthesized by the object detection and facial recognition classification. Computer vision-based human detection is possible with the help of a microprocessor development board, such as Raspberry Pi 3, and a passive infrared sensor [29]. The image of the person was captured by the camera module and transmitted over a mobile server for feature extraction, facial detection, and recognition in real-time [30]. The proposed design is implementable in hardware with a pre-existing wireless module and should be used to navigate the person or a robotic agent in a complex environment, such as a shopping mall or a warehouse to transport cargo. The RSSI based indoor navigation is an integral part of the robotic system to take the necessary action in a specific region. The contributions of this paper are as follows:  
1. Based on the observations and literature, the RSSI and wireless link quality values continuously varied with respect to the changes in the environment, and so a comparative analysis of different classification models was made for this method. 2. The designed framework was implemented in a pre-existing environment without hardware modification and with limited power usage. 3. The integrated cognition-based target selection and path planning framework was implemented. In Section 2, the data collection method, system flow, path planning, and facial and object recognition methods are explained. Section 3 describes the sample distribution, classification comparison, and prediction accuracy. Conclusions and future work are described in Section 4.