This dissertation presents the theory, design, implementation and successful deployment of a cognitive engine decision algorithm by which a cognitive radio-equipped mobile robot may adapt its motion and radio parameters through multi-objective optimization. This provides a proof-of-concept prototype cognitive system that is aware of its environment, its user's needs, and the rules governing its operation. It is to take intelligent action based on this awareness to optimize its performance across both the mobility and radio domains while learning from experience and responding intelligently to ongoing environmental mission changes. The prototype combines the key features of cognitive radios and autonomous vehicles into a single package whose behavior integrates the essential features of both. The use case for this research is a scenario where a small unmanned aerial vehicle (UAV) is traversing a nominally cyclic or repeating flight path (an â •orbitâ •) seeking to observe targets and where possible avoid hostile agents. As the UAV traverses the path, it experiences varying RF effects, including multipath propagation and terrain shadowing. The goal is to provide the capability for the UAV to learn the flight path with respect both to motion and RF characteristics and modify radio parameters and flight characteristics proactively to optimize performance. Using sensor fusion techniques to develop situational awareness, the UAV should be able to adapt its motion or communication based on knowledge of (but not limited to) physical location, radio performance, and channel conditions. Using sensor information from RF and mobility domains, the UAV uses the mission objectives and its knowledge of the world to decide on a course of action. The UAV develops and executes a multi-domain action; action that crosses domains, such as changing RF power and increasing its speed. This research is based on a simple observation, namely that cognitive radios and autonomous vehicles perform similar tasks, albeit in different domains. Both analyze their environment, make and execute a decision, evaluate the result (learn from experience), and repeat as required. This observation led directly to the creation of a single intelligent agent combining cognitive radio and autonomous vehicle intelligence with the ability to leverage flexibility in the radio frequency (RF) and motion domains. Using a single intelligent agent to optimize decision making across both mobility and radio domains is unified multi-domain decision making (UMDDM). / Ph. D.