This paper presents a class project that can be performed in a junior-level fluid mechanics course. Students gain experience in design and conducting experiments, and in reviewing the relevant technical literature. Experiments were conducted to determine the drag coefficient of a badminton shuttlecock. Two types of testing were conducted: wind tunnel tests of a full-scale model, and drop tests using a high-accuracy radar gun. The drag coefficients calculated from these measurements were then compared to the limited data available in the literature. The range of drag coefficients measured was from 0.55 to 0.65.
In this paper we introduce tumbling, a relatively unexplored method of locomotion in which the robot utilizes net body rotations while ambulating. Tumbling for mobile robots is attractive in that it can enable increased mobility on smaller scales, often while reducing hardware requirements. As motivation for this interesting form of locomotion we provide a geometric analysis of a vertical step climbing task, one that tumbling robots perform well with respect to their size and complexity. In addition to our analysis we present results of a hardware experiment with a tumbling robot performing the task for varying combinations of frictional coefficients at the step and ground.
Computer vision as an entire field has a wide and diverse range of applications. The specific application for this project was in the realm of dance, notably ballet and choreography. This project was proof-of-concept for a choreography assistance tool used to recognize and record dance movements demonstrated by a choreographer. Keeping the commercial arena in mind, the Kinect from Microsoft was chosen as the imaging hardware, and a pilot set chosen to verify recognition feasibility. Before implementing a classifier, all training and test data was transformed to a more applicable representation scheme to only pass the important aspects to the classifier to distinguish moves for the pilot set. In addition, several classification algorithms using the Nearest Neighbor (NN) and Support Vector Machine (SVM) methods were tested and compared from a single dictionary as well as on several different subjects. The results were promising given the framework of the project, and several new expansions of this work are proposed.
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