We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.

Artificial neural networks (ANNs, or simply NNs) are inspired by biological nervous systems and consist of simple processing units (artificial neurons) that are interconnected by weighted connections. Neural networks can be "trained" to solve problems that are difficult to solve by conventional computer algorithms. This paper presents the development and implementation of a generalized back-propagation multi-layer perceptron (MLP) neural network architecture described in very high speed hardware description language (VHDL). The development of hardware platforms has been complicated by the high hardware cost and quantity of the arithmetic operations required in an online MLP, i.e., one used to solve real-time problems. The challenge is thus to find an architecture that minimizes hardware costs while maximizing performance, accuracy, and parameterization. The paper describes herein a platform that offers a high degree of parameterization while maintaining performance comparable to other hardware based MLP implementations.

Among many approaches to address the high-level decision making problem for autonomous robots and agents, the robot programming and plan language Golog follows a logic-based deliberative approach, and its successors were successfully deployed in a number of robotics applications over the past ten years. Usually, Golog interpreter are implemented in Prolog, which is not available for our target platform, the bi-ped robot platform Nao. In this paper we sketch our novel prototype implementation of a Golog interpreter in the scripting language Lua. With the example of the elevator domain we discuss how the basic action theory is specified and how we implemented fluent regression or backtracking in Lua. One possible advantage of the availability of a Non-Prolog implementation of Golog could be that Golog becomes available on a larger number of platforms, and also becomes more attractive for roboticists outside the Cognitive Robotics community.

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.

This paper presents a simulator that is being developed to study the performance of certain types of vehicle navigation. The performance metric looks at a likelihood of accomplishing a task and the cost of the strategy – measuring both robustness and efficiency. We present results involving only autonomous control strategies, yet the simulator will be used to compare human performance in completing the same task.

Until recent years, the development of real-world humanoid robotics applications has been hampered by a lack of available mobile computational power. Unlike wheeled platforms, which can reasonably easily be expected to carry a payload of computers and batteries, humanoid robots couple a need for complex control over many degrees of freedom with a form where any significant payload complicates the balancing and control problem itself. In the last few years, however, an significant number of options for embedded processing suitable for humanoid robots have appeared (e.g. miniaturized motherboards such as beagle boards), along with ever-smaller and more powerful battery technology. Part of the drive for these embedded hardware breakthroughs has been the increasing demand by consumers for more sophisticated mobile phone applications, and these modern devices now supply much in the way of sensor technology that is also potentially of use to roboticists (e.g. accelerometers, cameras, GPS). In this paper, we explore the use of modern mobile phones as a vehicle for the sophisticated AI necessary for autonomous humanoid robots.

There is little doubt that the use of robots in introductory classes is an effective way to spark an initial interest in Computer Science and recruit students into our classes, and subsequently recruit some of them as Computer Science majors. But when the semester is over, the vast majority of our students are unlikely to see robots in the classroom again until they take advanced courses in AI or Robotics. It is time for those of us who are proponents of the use of robots in Introductory Computer Science to start thinking seriously about how we are using robots in our classes, and how to sustain the interest and enthusiasm of our students as they move on to more traditional courses. While the focus of this paper is on the use of robots in Introductory Computer Science courses, my goal is to initiate a more general discussion on the use of any sort of cool new technology (tangible or not) into both undergraduate and K-12 education. These technologies successfully attract students to study subjects that we ourselves are deeply engaged in. But we need to discuss as a community what happens when our individual classes conclude and the rest of their studies commence.

Many educators believe that activities centered on electronic tangibles (ET) and robots are fun and motivating for their students. However, it is often difficult, given the nature of both new hardware and new curricula to tease apart the nature and causes of this excitement. Formally planned educational evaluations can help build a deeper understanding of the effects of the new program on students. However, evaluating the impact of new ETs can be a challenge. Classes and workshops utilizing ETs as teaching devices are by their nature hands-on and may not lend themselves to traditional exam-based assessments. After all of the effort required to design a new ET, plan an educational experience utilizing the technology, and then implement that plan with students, evaluation is sometimes left as an afterthought. Strong evaluation methods can provide important insights into ways to improve a design and help to show the impact of a program, resulting in increased opportunities for funding, dissemination, and replication.

This paper reports on the Design Compass, a classroom tool for helping students record and reflect on their design process as they work on and complete a design challenge. The Design Compass software provides an interface where students can identify and record the various design steps they used while performing them, and add digital notes and pictures to document their work. In the Design Log view, students can review steps taken, and print the record of work done, which can be shared and discussed with their instructor or classmates. The paper describes the concepts underlying the creation of the Design Compass, its features as a metacognitive tool and how it works, and provides scenarios of its use as a teaching and assessment tool with eighth-grade technology education students, and in teacher professional development workshops.

This paper introduces the IRIS mobile robot project. IRIS is a largely student designed and implemented mobile robot platform created to provide a mechanism for classroom explorations of topics in artificial intelligence, cognitive science, and robotics. It has been designed to be used by students from middle school through college.