<p dir="ltr">This thesis lays the groundwork for multi-robot underwater operations by addressing key challenges in shallow-water environments, acoustic navigation, inter-robot communication, and automated performance evaluation in simulations. Using the Research-Oriented Underwater Glider for Hands-on Investigative Engineering (ROUGHIE) as a testbed, advancements were made to improve hardware reliability, maneuverability, and acoustic communication under multipath effects, propagation delays, and environmental noise. A comprehensive simulation environment, developed with ROS and Gazebo, was used to evaluate and optimize localization strategies. Notably, the Weighted Average with Dead Reckoning (WADR) method was introduced, merging dead reckoning with weighted average techniques to significantly reduce localization error and mitigate the impact of acoustic delays.</p><p dir="ltr">To validate these solutions, experiments were conducted using SeaTrac X110 and X150 acoustic modems, demonstrating the feasibility of surface-deployed acoustic beacons for energy-efficient localization without frequent resurfacing. Additionally, acoustic communication protocols were enhanced to support mission monitoring and control via standardized IMC messages. The developed data manager addressed payload limitations by implementing efficient packet management and error correction mechanisms.</p><p dir="ltr">Finally, the thesis introduced an automated performance evaluation framework, enabling real-time metric analysis and feedback for ROS-based simulations. This framework reduces manual labor for performance testing and grading, streamlining future development processes. Overall, this work provides a robust foundation for scalable multi-robot underwater systems and highlights opportunities for further advancements in autonomy, communication, and mission planning for Autonomous Underwater Vehicles (AUVs).</p>

Robotic industries are growing faster than in any other era with the demand and rise of in home robots or assisted robots. Such a robot should be able to navigate between different rooms in the house autonomously. For autonomous navigation, the robot needs to build a map of the surrounding unknown environment and localize itself within the map. For home robots, distinguishing between different rooms improves the functionality of the robot. In this research, Simultaneously Localization And Mapping (SLAM) utilizing a LiDAR sensor is used to construct the environment map. LiDAR is more accurate and not sensitive to light intensity compared to vision. The SLAM method used is Gmapping to create a map of the environment. Gmapping is one of the robust and user-friendly packages in the Robotic Operating System (ROS), which creates a more accurate map, and requires less computational power. The constructed map is then used for room categorization using Convolutional Neural Network (CNN). Since CNN is one of the powerful techniques to classify the rooms based on the generated 2D map images. To demonstrate the applicability of the approach, simulations and experiments are designed and performed on campus and an apartment environment. The results indicate the Gmapping provides an accurate map. Each room used in the experimental design, undergoes training by using the Convolutional Neural Network with a data set of different apartment maps, to classify the room that was mapped using Gmapping. The room categorization results are compared with other approaches in the literature using the same data set to indicate the performance. The classification results show the applicability of using CNN for room categorization for applications such as assisted robots.