This work seeks to contribute to software development education by motivating the use of engaging in-class and laboratory assignments.Ideally, these assignments should involve considerable student buy-in and should also evolve throughout the course to mimic real-world software development. Prior research is discussed, as well as several specific examples from two introductory programming classes. The ultimate contribution is a convincing argument to spend the extra effort to design better student projects.
This paper attempts to automate and replace human guidance in the control of a K-9 unit by modeling that guidance from observation. The ultimate research goal seeks to contribute toward the autonomous command of a trained K-9 unit by analyzing the movement and the behavior of the dog as it responds to command tones. Specifically, GPS and command signal information (from a human trainer) is recorded as a canine follows (or fails to follow) instructions as it moves toward a destination. The data is then processed into training instances and used as training data for a General Regression Neural Network (GRNN). Then, the network is used to classify previously unseen test instances to determine if the behavior at that moment is normal or anomalous (in need of correcting tones). Both representation of training instances and the system parameters of the GRNN are optimized using a simple Evolutionary Hill-Climber (EHC). Given even fairly limited initial data for training, the system performs well, producing relatively few false positives and false negatives in classification.
This work demonstrates the autonomous command of a trained search canine to multiple waypoints using a novel state machine control algorithm. A hardware system is utilized in order to interface with the Global Position Satellite (GPS) system and with a tone and vibration generator for the purpose of accurately navigating and commanding the canine. An operational control algorithm for autonomous guidance of the canine is described in detail. Empirical results of an autonomously commanded canine are demonstrated with an 73% mission success rate for simple paths and a 62% mission success rate for complex paths. This work demonstrates a novel way to expand the capabilities of canines in a wide variety of missions, including search and detection.
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