Robotic Autonomy is a seven-week, hands-on introduction to robotics designed for high school students. The course presents a broad survey of robotics, beginning with mechanism and electronics and ending with robot behavior, navigation and remote teleoperation. During the summer of 2002, Robotic Autonomy was taught to twenty eight students at Carnegie Mellon West in cooperation with NASA/Ames (Moffett Field, CA). The educational robot and course curriculum were the result of a ground-up design effort chartered to develop an effective and low-cost robot for secondary level education and home use. Cooperation between Carnegie Mellon's Robotics Institute, Gogoco, LLC. and Acroname Inc. yielded notable innovations including a fast-build robot construction kit, indoor/outdoor terrainability, CMOS vision-centered sensing, back-EMF motor speed control and a Java-based robot programming interface. In conjunction with robot and curriculum design, the authors at the Robotics Institute and the University of Pittsburgh's Learning Research and Development Center planned a methodology for evaluating the educational efficacy of Robotic Autonomy, implementing both formative and summative evaluations of progress as well as an indepth, one week ethnography to identify micro-genetic mechanisms of learning that would inform the broader evaluation. This article describes the robot and curriculum design processes and then the educational analysis methodology and statistically significant results, demonstrating the positive impact of Robotic Autonomy on student learning well beyond the boundaries of specific technical concepts in robotics.

Abstract-Current methods for modeling, analysis, and design of cyber-physical systems lack a unifying framework due to the complexity and heterogeneity of the constituent elements and their interactions. Our approach is to define relationships between system models at the architectural level, which captures the structural interdependencies and some semantic interdependencies between representations without attempting to comprehend all of the details of any particular modeling formalism. This paper addresses the issue of defining and evaluating consistency between architectural views imposed by various heterogeneous models and a base architecture (BA) for the complete system. This notion of structural consistency ensures that the model elements adhere to the cyber and physical types and the connections between components present in the BA, which serves as the unifying framework for model-based development. Consistency checking between a model and the underlying system architecture is formulated as a typed graph matching problem between the connectivity graphs of the corresponding architectural view and the system's BA. The usefulness of the approach to check system modeling assumptions is illustrated in the context of two heterogeneous views of a quadrotor air vehicle.

Cyber-physical systems (CPS) are heterogeneous, because they tightly couple computation, communication, and control along with physical dynamics, which are traditionally considered separately. Without a comprehensive modeling formalism, modelbased development of CPS involves using a multitude of models in a variety of formalisms that capture various aspects of the system design, such as software design, networking design, physical models, and protocol design. Without a rigorous unifying framework, system integration and integration of the analysis results for various models remains ad hoc. In this paper, we propose a multi-view architecture framework that treats models as views of the underlying system structure and uses structural and semantic mappings to ensure consistency and enable system-level verification in a hierarchical and compositional manner. Throughout the paper, the theoretical concepts are illustrated using two examples: a quadrotor and an automotive intersection collision avoidance system.

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