Determining the position of a robot in an environment, termed localization, is one of the challenges facing roboticist. Localization is essential to solving more complex problems such as locomotion, path planning and environmental learning. Our lab is developing a multi-agent system to use multiple small robots to accomplish tasks normally completed by larger robots. However, because of the reduced size of these robots, methods previously used to determine the position of the robot, such as GPS, cannot be employed. The problem we are facing is that we need to be able to determine the position of each of the robots in this multi-agent system simultaneously. We have developed a system to help track and identify robots using an overhead camera and LEDs, mounted on the robots, to efficiently solve the localization problem.

Mobile robots are becoming more relevant and an essential part of our everyday lives. They are increasingly taking their place in service-oriented applications including domestic and entertainment roles. They are beginning to open up many potential opportunities, but they still come with challenges in terms of their limited sensing capability and accuracy. In this project, we addressed these fundamental problems with mobile robotics and demonstrate our approach to each of the problems with a mobile robot equipped with low-cost and low-end devices. The r-one swarm robot is a low-cost multi-robot systems platform that is advanced enough for multi-robot research, robust enough for undergraduate and graduate education and cheap enough for K-12 outreach. As robots become more and more useful, multiple robots working together on a single task will become commonplace. Many of the most useful applications of robots are particularly well-suited to this “swarm” approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally different ways than robots working individually. However, swarms of robots are difficult to program and coordinate.

Children with cerebral palsy have difficulty moving their hands and muscles due to developmental issues. One way to assist these children is by having them participate in physical therapy. The best form of physical therapy for children is playing. Playing is a natural activity for children, and it also helps in furthering the developments of muscles. This form of therapy is perhaps a greater choice for children because it keeps the child engaged due to the interest the child holds in the activity. By integrating two projects done by previous students, a Pleo that is controlled by a Wii Nunchuk will be able to teach Pleo how to dance. The child will be engaged in this activity for long durations because there are many variations of dance that the Pleo can learn by moving many body parts. Children using this toy will have continuous movement in their arm muscles by moving the Nunchuk for the duration of the activity. This toy will not only help children with severe disabilities feeling equal to their non-disabled peers by allowing them to use controllers found on many game consoles, but it will also enhance the child’s self-esteem and confidence by allowing them to control the outcome of the Pleo.

In recent years there has been a significant decline in the number of college students choosing majors in computer science or technology related fields. Although this trend is beginning to turn around at the undergraduate level, there remains disparity in the number of under-represented minority students who earn graduate degrees as compared to majority students. Additionally, within the United States, there is an achievement gap between under-represented minority students and majority students at a time when underrepresented groups are becoming an increasing proportion of the national labor force. This reluctance to study Science, Technology, Engineering, and Mathematics (STEM) disciplines must be confronted and changed if the United States is to maintain a competitive position within the global market. Effective use of learning technologies is vital to solving many of our current STEM learning challenges. Robotics is a growing research area in computer science education. We use robotics as a technology tool captivate and engage students in research in robotics.

Calliope is an open source mobile robot designed in the Tekkotsu Lab at Carnegie Mellon University in collaboration with RoPro Design, Inc. The Calliope5SP model features an iRobot Create base, an ASUS netbook, a 5-degree of freedom arm with a gripper with two independently controllable fingers, and a Sony PlayStation Eye camera and Robotis AX-S1 IR rangefinder on a pan/tilt mount. We use chess as a test of Calliope’s abilities. Since Calliope is a mobile platform we consider how problems in vision and localization directly impact the performance of manipulation. Calliope’s arm is too short to reach across the entire chessboard. The robot must therefore navigate to a location that provides the best position to access the pieces it wants to move. The robot proved capable of performing small-scale manipulation tasks that require careful positioning.

Search algorithms such as Rapidly-exploring Random Trees (RRTs) are common in robot programming. Including graphical representations of the output of these algorithms in a robotics framework can make the algorithms more accessible to students, and can also help programmers analyze and account for unexpected results. For this project, we used the Tekkotsu open source robot programming framework, available at Tekkotsu.org. We extended Tekkotsu’s graphical user interface for displaying vision data and maps to also display the output of an RRT search. We created several demos using two types of searches: one from a navigation path planner, and one from an arm path planner. In some cases the search had no solution, and the graphical output helped to illustrate why. This confirms the utility of the RRT visualization for explaining unexpected search results. We expect that this tool will also contribute to improved student understanding of the search algorithm.

The praying mantis uses its front legs for locomotion, prey capture and feeding. Inspired by this dexterity, we began designing a hexapod robot that could use its front legs for both locomotion and manipulation. Our current work focuses on the middle and back legs of the robot. We designed a five degree of freedom leg, using a gimbal to form three intersecting axes of rotation at the hip to imitate a ball-and-socket joint. There is also a one degree of freedom knee, and an unpowered ankle joint. A key requirement for the design is to provide for standing postures in which the robot can support itself without putting any load on the leg servos. This will increase servo life span. We simulated the leg by constructing a 3D model in SolidWorks, then importing that model into the Mirage simulator, part of the Tekkotsu robotics framework. A functioning prototype was then built using Robotis Dynamixel RX-64 servos. This was a geometrically simplified version of the original model, but it retained every motor capability of the original design. We tested the prototype using two types of pre-specified motion sequences, with good results.

This paper suggests and supports a design idea for improving dynamical navigation: adding an intermediary, adaptive obstacle representation level between perception and repeller representations. We illustrate our idea with our specific example of an adaptive obstacle representation level, which cleanly integrates into multiple existing navigation systems, treating each perceived obstacle entity as a locally sensitive, obstacle-valued function that returns an obstacle representation upon which steering and obstacle avoidance are based. Moreover, other elements of the navigation systems remain unaltered, thus preserving and extending original design virtues such as behavioral flexibility, computational efficiency, and dynamic responsiveness. Extensive simulations, validated with tests of real robots, demonstrate that our new representations compare favorably to previously employed representations on measures of effectiveness within a tested scenario, robustness over varying scenarios and ranges of parameter values, and computational efficiency.

Robotics is a multidisciplinary area of study across computer science, electrical engineering, and mechanical engineering. Robotics covers the study, design, manufacture, and use of robots in various applications. Robotics, computer vision, activity recognition, path planning, and the many other disciplines where computers interface to physical environments have proven to be a major source of inspiration and crucial new insights into artificial intelligence. Human-robot interaction has become a major concern as many robots have been used in real-world applications. For this year, this track features a number of robotics projects of Summer Research Experience for Undergraduate from the Advancing Robotics Technology for Societal Impact Alliance.

The accuracy of an automated reading tutor in tracking the reader’s position is affected by phenomena at the frontier of the speech recognizer’s output as it evolves in real time. We define metrics of real-time tracking accuracy computed from the recognizer’s successive partial hypotheses, in contrast to previous metrics computed from the final hypothesis. We analyze the resulting considerable loss in real-time accuracy, and propose and evaluate a method to address it. Our method raises real-time accuracy from 58% to 70%, which should improve the quality of the tutor’s feedback.