A Software and Hardware System for the Autonomous Control and Navigation of a Trained Canine

Abstract

This dissertation demonstrates the autonomous command and navigation of a trained canine to multiple waypoints. A system is described consisting of a canine that can be guided autonomously to a number of waypoints by an automatic software control algorithm. A hardware system has been developed in order to interface with GPS, accelerometers, gyroscopes, magnetometers, and tone and vibration generators for the purpose of accurately commanding and dictating the motion, path, and commands given to a canine. A canine has been trained to e ectively follow audio and vibration commands for guidance with a high degree of accuracy (71% mission success for simple paths and 63% mission success for complex paths). Both a Neural Networks approach and a State Machine Based approach to canine anomaly detection are presented, as well as strategies for anomaly correction. An operational control algorithm for autonomous guidance of the canine is described in detail. Finally, 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.