As research and development (R&D) in autonomous systems progresses further, more interdisciplinary knowledge is needed from domains as diverse as artificial intelligence (AI), bi-ology, psychology, modeling and simulation (M&S), and robotics. Such R&D efforts are necessarily interdisciplinary in nature and require technical as well as further soft skills of teamwork, communication and integration. In this paper, we introduce a 14 week, summer long internship for developing these skills in undergraduate science and engineering interns through R&D. The internship was designed to be modular and divided into three parts: training, innovation, and application/integration. The end result of the internship was 1) the development of an M&S ecosystem for autonomy concepts, 2) development and robotics testing of reasoning methods through both Bayesian methods and cognitive models of the basal ganglia, and 3) a process for future internships within the modular construct. Through collaboration with full-time professional staff, who actively learned with the interns, this internship incorporates a feedback loop to educate and per-form fundamental R&D. Future iterations of this internship can leverage the M&S ecosystem and adapt the modular internship framework to focus on different innovations, learning paradigms, and/or applications.

Spatial domain log-polar approaches have demonstrated success for video image registration. However, the log-polar representation is sensitive to origin location. This drawback often necessitates performing a parameter sweep over log-polar origin location, which can be time-consuming. In this paper we present an alternative approach that is appropriate for small to moderate scale, rotational, and skew misalignments but allows large translational offset. We use a form of robust phase correlation to estimate the gross translation, then perform a local search over log-polar origin to fine tune the translation. We sequentially estimate affine transform parameters by maximizing a measure of registration solution verity. We also investigate the effect of scale and rotational initial alignment errors on the robustness of the initial phase correlation to estimate gross translation. We present results using video imagery acquired from a real aerial video surveillance system.