Karina Rivera

Date of Award


Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Graduate Studies

Committee Chair

Dr. Hever Moncayo

First Committee Member

Dr. Richard Prazenica

Second Committee Member

Dr. Snorri Gudmundsson


In recent years, the interest of investigating intelligent systems for Unmanned Aerial Vehicles (UAVs) have increased in popularity due to their large range of capabilities such as on-line obstacle avoidance, autonomy, search and rescue, fast prototyping and integration in the National Air Space (NAS). Many research efforts currently focus on system robustness against uncertainties but do not consider the probability of readjusting tasks based on the remaining resources to successfully complete the mission. In this thesis, an intelligent algorithm approach is proposed along with decision-making capabilities to enhance UAVs post-failure performance. This intelligent algorithm integrates a set of path planning algorithms, a health monitoring system and a power estimation approach. Post-fault conditions are considered as unknown uncertainties that unmanned vehicles could encounter during regular operation missions. In this thesis, three main threats are studied: the presence of unknown obstacles in the environment, sub-system failures, and low power resources. A solution for adapting to new circumstances is addressed by enabling autonomous decision-making and re-planning capabilities in real time.