Master of Science (MS), Ohio University, 2024, Mechanical Engineering (Engineering and Technology)

Aerial Vehicle (UAS) remote and autonomous operation is a growing industry with applications in defense and the commercial space. UAS's can use the Global Positioning System (GPS) to determine their position when signals are available. Operational environments like urban canyons, forested areas, and indoors can block the UAS from receiving GPS signals and prevent the receiver from determining a position, which can hinder the vehicle's ability to successfully complete the mission. Unreliable positioning from GPS data requires UAS's to have an alternative means of localization, which can be accomplished by utilizing ultra-wideband ranging to landmarks and other aerial systems. Machine learning can be used to train models to utilize raw data from intervehicle distance sensors on the agent and anchors to determine the location of the agent without access to GPS data. This work will explore using machine learning as an adaptable localization system using neural networks and gradient descent methods. Fully trained neural networks will be capable of learning specific noise models and performing cooperative localization using unfiltered intervehicle ranges and anchor positions.

Committee: Jay Wilhelm (Advisor); Sergio Ulloa (Committee Member); Dusan Sormaz (Committee Member); Chris Bartone (Committee Member) Subjects: Aerospace Engineering; Computer Science

Master of Science (MS), Ohio University, 2021, Mechanical Engineering (Engineering and Technology)

Navigation in GPS denied environments is notoriously difficult for small UAVs due to reduction of visible satellites and urban canyon multi-path interference. Several existing methods can be used for navigating in a constrained environment, but they often require additional specific sensing hardware for a localization solution or only provide local frame navigation. Autonomous systems often include LiDAR and RGB cameras for mapping, sensing, or obstacle avoidance. Utilizing these sensors for navigation could provide the only or complimentary localization solutions to other GPS denied localization methods in a global or local frame, especially in urban canyons where unique landmarks can be identified. Information from scanning LiDAR can be correlated with camera pixel coordinates and used to range unique visual landmarks that have known locations. The present work included surface function fitting to reduce ranging error to spherical landmarks since multiple lasers were able to range each landmark. Simulation and experimental validation of the unique camera-LiDAR modified trilateration process was undertaken using colored light orbs as landmarks with a 16-laser scanning LiDAR and known positions. Position error was computed and verified that the position estimate process was successful at varying landmark configurations and viewing angles in simulation. Experimental results verified the process while also providing higher accuracy than a previous method of using a single point on landmark surfaces, for the tested setup.

Committee: Jay Wilhelm (Advisor); Daniel Gauthier (Committee Member); Robert Williams Jr. (Committee Member); Douglas Lawrence (Committee Member) Subjects: Robotics; Robots

MS, University of Cincinnati, 2018, Engineering and Applied Science: Mechanical Engineering

This thesis focuses on localization of mobile robots in indoor environments without the use of pre-deployed sensor networks. The localization of mobile robots in indoor environment is very difficult due to the absence of Global Positioning System (GPS) signals. The problem of localization in indoor environments is usually solved using Simultaneous Localization and Mapping (SLAM) algorithms. However, these algorithms often prove to be insufficient in complex and dynamic environments. An example of such environment is a tunnel which does not provide distinguishing environmental features for the SLAM algorithms to work properly. The absence of visible light makes it difficult to use visual sensors such as cameras. In such environments, without the use of pre-deployed sensor networks, it is very difficult to obtain localization of the robot. This thesis proposes the use of active deployment of landmarks by the robot itself. The robot is assumed to have a physical capacity of carrying Radio Frequency (RF) Beacons which are deployed in the environment based on the calculations of the predicted co-variance of position error. The robot tries to achieve its goal based on the combination of data from the encoder and RF beacons. The system of transmitting RF beacons is deployed by the mobile robot which carries the receiver beacon as it moves through the environment. Using a combination of Dead Reckoning and tri-lateration of position using the RF beacons in the framework of Extended Kalman filter, the robot localized in the environment. As the RF beacons are deployed by the mobile robot, their locations are approximated using Levenberg- Marquardt algorithm. The mobile robot monitors the estimate of its localization error which is then used to make decisions to deploy successive beacons. The operative structure of the mobile robot is provided in the thesis which could be used to achieve desired navigation.

Committee: Manish Kumar Ph.D. (Committee Chair); Rui Dai Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Robots

Master of Science, University of Toledo, 2014, Engineering (Computer Science)

Optimization algorithms are truly complex procedures that consider many elements when optimizing a specific problem. Cloud computing (CCom) and Wireless sensor networks (WSNs) are full of optimization problems that need to be solved. One of the main problems of using the clouds is the underutilization of the reserved resources, which causes longer makespans and higher usage costs. Also, the optimization of sensor nodes' power consumption, in WSNs, is very critical due to the fact that sensor nodes are small in size and have constrained resources in terms of power/energy, connectivity, and computational power. This thesis formulates the concern on how CCom systems and WSNs can take advantage of the computational intelligent techniques using single- or multi-objective particle swarm optimization (SOPSO or MOPSO), with an overall aim of concurrently minimizing makespans, localization time, energy consumption during localization, and maximizing the number of nodes fully localized. The cloudlet scheduling method is implemented inside CloudSim advancing the work of the broker, which was able to maximize the resource utilization and minimize the makespan demonstrating improvements of 58\% in some cases. Additionally, the localization method optimized the power consumption during a Trilateration-based localization (TBL) procedure, through the adjustment of sensor nodes' output power levels. Finally, a parameter-study of the applied PSO variants for WSN localization is performed, leading to results that show algorithmic improvements of up to 32\% better than the baseline results in the evaluated objectives.

Committee: Mansoor Alam (Committee Chair); Robert Green II (Committee Co-Chair); Weiqing Sun (Committee Member); Vijay Devabhaktuni (Committee Member) Subjects: Artificial Intelligence; Computer Science; Engineering