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A Robust Approach for Enhanced Autonomous Robot Navigation
Ismail, Sherif Khaled Mohamed Issa
Ismail, Sherif Khaled Mohamed Issa
Date
2024-03
Advisor
Type
Thesis
Degree
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35.232-2024.38a Sherif Khaled Mohamed Issa Ismail.pdf
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Description
A Master of Science thesis in Mechatronics Engineering by Sherif Khaled Mohamed Issa Ismail entitled, “A Robust Approach for Enhanced Autonomous Robot Navigation”, submitted in March 2024. Thesis advisor is Dr. Mamoun Abdel-Hafez. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
The demand for robust and precise autonomous navigation systems for mobile robots has become increasingly imperative. These systems find applications across a spectrum of industries, from logistics and transportation to agriculture and defense. Autonomous navigation offers the promise of enhanced efficiency, reduced operational costs, and a safer work environment, making it a vital component in the ever-evolving landscape of modern automation. This research aims to enhance the localization accuracy of Unmanned Ground Vehicles (UGVs) with cost-effective Global Positioning System (GPS) and Inertial Navigation System (INS) sensors by proposing a variation of the Kalman Filter (KF) for sensor fusion. This algorithm is used in this work for a low-cost loosely coupled GPS/INS integration. To achieve this objective, we propose the utilization of the Variational Bayesian-based Maximum Correntropy Cubature Kalman filter (VBMCCKF). This filtering technique demonstrates exceptional adaptability and resilience, making it particularly well-suited for the dynamic and often unpredictable environments that autonomous vehicles must navigate. The research strategy encompasses a dual-phase approach. Initially, a comprehensive evaluation of the performance of EKF, CKF, VBCKF, and VBMCCKF algorithms is conducted in a distinct application, namely Li-ion battery estimation. This preliminary phase is aimed at establishing the efficacy of these algorithms. Subsequently, in the core application, an offline estimation will be carried out using experimentally derived vehicle trajectories to ensure meticulous accuracy verification. It is noteworthy that the VBCKF as well as the VBMCCKF are being applied in this particular application for the first time. Following this, a transition was made towards real-time deployment for the localization of the autonomous navigation of a UGV. Throughout this phase, a thorough assessment of practicality and resilience are undertaken in dependable operational scenarios.