Alexis P.

This project featured a seesaw-like mechanism with a single degree of freedom. It utilized an IMU sensor and was powered by two brushless motors with drone propellers at each end of the seesaw. The control strategy was entirely developed using fuzzy logic. The set point was determined by the rotation angle detected by a Kinect sensor, which recognized the user mimicking an airplane pilot's stance, simulating the aircraft's yoke with their hands. This angle was then transmitted to the balancer. The project was showcased at a technology fair at my local university, captivating the attention of attendees

This project involves developing a sophisticated system for autonomous path navigation and obstacle detection in mobile vehicles. Using advanced image processing, the Path Extraction Algorithm (PEA) identifies viable driving routes. The Environment Extraction Algorithm (EEA) processes 2D laser scanner data to determine the vehicle's spatial positioning and detects obstacles. Additionally, the Pattern Classification Algorithm (PCA), utilizing supervised machine learning and Artificial Neural Networks, classifies various road patterns. These components collectively form the Navigation-Path Extraction Algorithm (NPEA).