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Object Detection, Localization and Navigation Strategy for Obstacle Avoidance Applied to Autonomous Wheelchair Driving
Department: Mechanical Engineering
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Paper000
Specimen Elements
Pocatello
Unknown to Unknown
Nusrat Farheen
Idaho State University
Thesis
No
4/27/2023
digital
City: Pocatello
Master
The primary aim of this study was to develop machine learning or deep-learning aided procedures along with scientific investigations that enhances the capability of a commercial non- autonomous wheelchair towards autonomy. The thesis addresses the computer vision work for obstacle detection and localization applied to an autonomous wheelchair operation. The computer vision tasks including the depth image classification are accommodated in a small form factored and resource constraint computers such as Raspberry Pie and Google Coral. The tasks and strategies also include classifying the images using a pretrained model (TensorFlow lite), detecting and measure the degree of obstacle avoidance by pairing color (RGB) image classification with depth images. The thesis also offers approaches for indoor localization applicable for the autonomous wheelchair development. The objective has been further extended to develop a simulation platform for autonomous wheelchair driving where navigation and path mapping construction algorithm evaluations are visually offered using MATLABĀ®. In addition, the thesis includes research and project contributions prior to the change of thesis subject to autonomous wheelchair development. These contributions are addressed as the additional works which includes (1) the initial work on error determination in motion capture process using VICON and (2) a wafer alignment fault detection process using image processing. Keywords: machine learning, computer vision, autonomous wheel-chair, navigation, mobile robot, depth image, obstacle avoidance.

Object Detection, Localization and Navigation Strategy for Obstacle Avoidance Applied to Autonomous Wheelchair Driving

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