ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Master of Science in Aerospace Engineering


Mechanical and Aerospace Engineering

First Advisor

Atilla Dogan

Second Advisor

Manfred Huber


Recent advancements on the unmanned systems manifest the potential of these technologies to impact our daily life. In particular, the unmanned aircraft systems (UAS) become ordinary for people in almost any area from aerial photography to emergency responses, from agricultural services to even autonomous deliveries. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. Easiness and affordability to access these systems accelerated the innovations and the novel ideas for the solution of diverse real-life problems. Despite its benefits, however, this widespread availability also resulted in the safety and regulatory concerns in general. In an autonomous flight task over a public space, besides the mission objectives and the benefits, concerns regarding the public safety, privacy, and the regulations have to be addressed systematically during the planning and considered in the decision-making process. Therefore, there is a need for a comprehensive framework that can properly quantify and assess the risks incurred by the UAS operations to these concerns. This thesis presents the development of a probabilistic risk assessment framework and a path planning implementation of a concept of Safe Task-Aware Autonomous Resilient Systems (STAARS) to address the safety concerns. STAARS is conceptualized to consider the safety by quantifying and assessing the risks, task-awareness by adapting different tasks and environments, and resiliency by withstanding and making decisions in adversarial conditions. As a result, a multi-objective decision-making capability is introduced in this concept. The aim of the thesis is to establish a framework that could be used for the path planning of UAS operations to quantify, assess and compare the risks incurred by these operations as well as the profits of the mission objectives such that a multi-objective optimization can be achieved with a task-level decision-making capability. The proposed framework consists of the risk assessment part where a probabilistic risk exposure concept and the UAS failure mode analysis are utilized, and a generic utility-based approach for the multi-objective optimization part. In the next step, a commonly used path planning algorithm, which is rapidly-exploring random trees (RRT), is introduced. Finally, the implementation of the proposed framework for a couple of simple UAS scenarios are demonstrated using the path planner.


Probabilistic Risk Assessment Framework, UAS failure modes, UAS ground safety analysis, Safe path planning, Risk exposure modeling, Multi-objective utility optimization


Aerospace Engineering | Engineering | Mechanical Engineering


Degree granted by The University of Texas at Arlington