# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 1 Introduction  
In an indoor environment, satellite GPS signals do not have adequate strength due to multipath reflection from indoor objects [1; 2; 3; 4]. Radio frequency identification (RFID)-based localization technology needs an RFID tag placed over a certain area for a randomly moving agent, and identifies the position and makes decisions [5; 6]. However, if the agent misses the tag, then it is hard to achieve accurate localization. The ultra-wideband and infrared-based localization methods have higher accuracy than the RFID based methods [7]. Identifiable signatures, such as Wi-Fi, escalators, and high tension transmission lines influence sensor-based indoor navigation [8]. [PERSON] et al. suggested that visible light communication through LED can be precise in tracking individuals in an indoor area [9] and Complementary Metal Oxide Semiconductor (CMOS)-based cameras decode the information transmitted [10]. The movement in the dynamic environment can be sensed by a change in the data rate. As the distance increases the received power (in dB) decreases [11]. The power spectral density (dBm/MHz) is also affected by the increment in the distance. The use of a power amplifier reduces the reduction rate of received power with respect to distance [12]. The link quality can be measured from different key performance evaluation parameters, such as received signal strength index (RSSI), link quality index (LQI), signal to noise ratio (SNR), and bit error rate (BER). These parameters aredirectly provided by the transceivers and the significance of such parameters varies with hardware placements and different vendors provide different weights to these parameters [13]. [PERSON] et al. stated that merely RSSI signal improvement may not lead to position accuracy and suggested media access control (MAC) addresses for higher accuracy in buildings. The RSSI based localization method with a hybrid model of Gaussian progress regression and convolutional neural network (CNN) method outperforms the nearest neighbors investigation method [14]. Random forest-based forecasting converges more possible inputs than linear regression, multiple linear regression, and CNN networks [15]. [PERSON] et al. used four transmitter nodes to scan and test the area (10.945 m \(\times\) 6.401 m) for two different test cases, that is, linear movement in two different directions: north-south and east-west, intending to detect human obstacles and correct them with different artificial intelligence algorithms. The result confirmed the sudden blockages of more than 87% when using a sliding window received signal strength index (RSSI) method over-sampled inputs. Human presence reduced the RSSI values during mapping and also at the time of localization in real-time, and this error was corrected with the help of classification methods; accuracies of 2.3 m and 3.8 m for north-south and east-west, respectively, were achieved [16]. [PERSON] et al. suggested hybrid wireless (HW) fingerprint localization wherein the ratio of RSSI for different access points is determined, and CNN (convolutional neural network) architecture is applicable for optimal results from inputs. Large datasets are collected with thousands of entries for a 12.5 m \(\times\) 10 m area using nine receiving points for half a month. The average positioning errors with a hybrid wireless (HW) fingerprint are 4.1681, 4.1145, and 3.9118 m using KNN, SVM, and CNN classification methods respectively. The accuracy of the CNN based localization method depends upon parameters like the activation function, learning rate and threshold process. For better accuracy, these parameters have to check for each entry in the database, which is a prolonged process. Therefore, this method may be accurate in specific conditions but is not preferable in general [17]. [PERSON] et al. proposed an RSSI based mobile robotic platform based on the kernel extreme learning machine (K-ELM) algorithm to localize the robotic system for good accuracy and low cost of implementation.