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A s a field, computer science faces a problem. From 2000 to 2004, the percentage of first-year undergraduates planning to major in CS declined by more than 60 percent (see the "Declining Interest in Computer Science" sidebar). 1 To attract more students, the introductory CS curriculum must be motivating and relevant. CS courses that are set in a motivating context (for example, using multimedia, gaming, or robotics) can excite students and get them hooked. Other researchers have worked on introductory programming classes with robots as well as introduction to robotics classes (http://myro. roboteducation.org/robobiblio). We didn't want to create a robotics course but rather an introductory CS course based on robots. Introduced properly, robots make visible and tangible those aspects of CS that are often hidden behind computer screens and in computer memory. To further this goal, we formed the Institute for Personal Robots in Education (IPRE), a joint effort between Georgia Tech and Bryn Mawr College and sponsored by Microsoft Research (www.roboteducation. org). This article discusses the first-year results of a three-year project.

We have developed a CS1 curriculum that uses a robotics context to teach introductory programming [1]. Core to our approach is that each student has their own personal robot. Our robot and software have been specifically developed to support the needs of a CS1 curriculum. We frame traditional problems (robot control) in terms that are personal, relevant, and fun. Initial trial classes have shown that our approach is successful and adaptable.

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We investigate a technique that uses an embedded network deployed pervasively throughout an environment to aid robots in navigation. First, we show that the path computed by the network is useful for a simple mobile robot. The robot uses a network of 156 nodes to navigate through a complex, dynamic, environment. This is the largest embedded network used for navigation we are aware of. In our approach, the network nodes do not need to know their absolute or relative positions and the mobile robots do not build any kind of map. Second, the impact of specific network deployments on path quality is examined. Two types of arrangements, hexagonal and rectangular, in two different environments are considered. We present quantitative results collected from a real-world embedded network of 60 nodes. Experimentally, we find that on average, the path computed by the network is only 24% longer than the optimal path. Also, we find a slight advantage for the hexagonal arrangement.