Sun, QiaoVincent, Isabelle2017-12-182017-12-182008Vincent, I. (2008). Intelligent mobility controllers for a robotic vehicle climbing obstacles autonomously (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/2218http://hdl.handle.net/1880/103219Bibliography: p. 81-86Research in mobile robot navigation has demonstrated some success in navigating a flat world while avoiding obstacles. However, algorithms which analyze complex environments in order to climb obstacles have had very little success due to the complexity of the task. This thesis aims to design a controller for a mobile robot to autonomously climb obstacles by adapting its geometric configuration. Three control algorithms are proposed to solve the autonomous locomotion problem for climbing obstacles. A reactive controller evaluates the appropriate geometric configuration based on terrain and vehicle geometric considerations. As a scripted controller is difficult to design for every possible circumstance, learning algorithms are a plausible alternative. A neural network based controller works if a task resembles a learned case. However, it lacks adaptability. Learning in real-time by reinforcement and progress estimation facilitates robot control and navigation. This thesis presents the reinforcement learning algorithm developed to find alternative solutions when the reactive controller gets stuck while climbing an obstacle. The controllers are validated and compared with simulations.xii, 88 leaves : ill. 30 cm.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.master thesis10.11575/PRISM/2218