Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of $1.73\mathrm{m}$, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: $10.35\mathrm{m}$) and Nonparametric Information (NI) filter with variable acceleration (RMSE: $5.2\mathrm{m}$) under the same experiment environment.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of $\le 2.16$ m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as $1.55$ m and $1.97$ m with $83.33\%$ and $81.05\%$ of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

Subterranean environments such as caves, tunnels, and underground urban structures present significant challenges for search and rescue operations due to hazards like unstable terrain, limited visibility, and the absence of GPS signals. To address these challenges, autonomous mobile robots have emerged as a viable solution. This paper presents a modular autonomy stack for autonomous underground exploration, object detection and localization in simulated hazardous subterranean environments. The system integrates three core modules: LIO-SAM for lidar-IMU odometry, GBPlanner2 as the exploration planner with a pure-pursuit tracker for path following, and YOLOv3 for real-time artifact detection with depth-based localization. We evaluate the integrated stack in multiple virtual circuits (cave, tunnel, and urban) and report circuit-wise performance, including odometry drift, exploration coverage, and detection errors. The system performs best in the urban circuit, where it localizes multiple artifacts with low horizontal drift. Performance degrades in steep or cluttered terrain, where limited observability and collision-checking errors reduce coverage and increase failure rates.