A project-centered introductory computer course for freshman and sophomore engineers has been developed at the Penn State Abington College. Students form teams to collaboratively design and implement algorithms in the C language for autonomous mobile robots. The team projects have been developed to "teach" critical programming concepts. These team projects have largely replaced the traditional lecture portion of the course. Collaborative software design for mobile robots provides direct, visual feedback of algorithms and has enhanced the learning process in the computer science course.
Development of a MATLAB Robotics Toolbox ROS interface and educational resources for a low-cost robot arm (Dobot Magician) in a senior-level robotics design course are described. The objectives of the study were to evaluate the effectiveness of a MATLAB interface to ROS services to control an articulated robot manipulator and conveyor belt in a laboratory setting. Laboratory exercises are described that expose students to the MATLAB/ROS interface, the basics of robot manipulator programming and an introduction to computer vision. Student survey data shows a positive response to the MATLAB/ROS strategy with the robot. The significance of this study is that a low-cost robot arm with a professional-level ROS/MATLAB software interface can greatly improve student access to advanced, hands-on, project-based education in intelligent manufacturing and Industry 4.0.
is an associate professor of engineering at the Penn State Abington campus where he teaches courses in electrical and computer engineering, computer science, and information sciences and technology. His research interests are mobile robotics, computer vision, intelligent systems, virtual world technology and innovative education.
Penn State Abington has integrated the student use of personal digital assistant (PDA) technology to foster active and collaborative learning experiences in the classroom and laboratory. PalmTM PDA technology was introduced into a sophomore-level digital systems course in the fall of 2000. The students have investigated handheld software tools for enhancing learning and instruction in both the lecture and laboratory components of the course. Handheld databases, simple CAD tools, C programming, image capture, web-based tools, and robotics applications have been explored. Index Termsactive learning, digital design, handheld computer, Personal Digital Assistant, PDA.
This study evaluated the use of Terf® virtual world technology to support student communication, collaboration, and engagement in an online undergraduate, sophomore-level computer engineering course in digital design. Virtual world technology provides a persistent 3D immersive environment in which students and faculty log into the platform with personalized avatars and enter customized virtual workspaces that support avatar navigation, text chat, voice communication, shared resources, and a webcam. Most importantly, the Terf® product allows student teams to work collaboratively on shared documents and images, facilitating team projects and improving student interaction and communication. The virtual collaboration tool also supported interaction with 3D models, virtual office hours, virtual lectures, and a team final project virtual poster session. Overall, the feedback from the students was positive and the students judged the tool as beneficial to improving student interaction and collaboration in the online course.
The selection of an uncertainty management system is a critical factor in the design, development, and maintenance of any experi system that is expected to perform reasoning with uncertain@ The goal of this paper is to provide insights into the implementation issues surrounding uncertainty management to aid in the design and development of an acoustic signal interpretation expert system. The paper provides a brief introduction and overview of the current schemes available for representation of uncertainty in knowledge-based systems. The results of a survey of the current efforts in acoustic signal interpretation with special attention directed toward the methods of uncertainty handling are also presented. Finally, issues relevant to the selection of uncertainty management schemes and assessing the impact on expert system design and development are discussed.
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