2026-09-23T11:44:27Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/120463 2023-09-05 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Kim, Joohyung 2023-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01 The student, Omar Darwish, accepted the attached license on 2023-05-04 at 13:36. The student, Omar Darwish, submitted this Thesis for approval on 2023-05-04 at 13:49. This Thesis was approved for publication on 2023-05-04 at 16:55. DSpace SAF Submission Ingestion Package generated from Vireo submission #19330 on 2023-09-01 at 17:16:10 Exploration of a data-driven walking controller for a large-scale bipedal robot with structural deformation Darwish, Omar 2023-05-04 Robotics Data-driven Controller Bipedal Robots Biped robots hold a great potential in advancing multiple industries, from healthcare to disaster relief to entertainment and personal assistance. These robots have the ability to navigate complex environments, interact more efficiently with people, and perform tasks that require balance and dexterity. However, accomplishing those tasks depends on heavily on being able to represent the kinematics of the robot, which can be heavily altered due to structural deformation and motors backlash. This thesis develops a data- driven approach to address the kinematics issues for a large-scale biped robot using Hybird-Leg as well as develop a simple controller for its locomotion. In order to evaluate the proposed approach, two sets of tests are proposed. First, a test is performed to compare between the performance produced when only using the existing kinematics model versus when using the kine- matics model along with an Artificial Neural Network (ANN) model, which showed that the developed approach improved the tracking. The next set of tests develops and validates a simple walking controller using simulation. Then, the generated walking trajectory is inputted into the ANN model in order to be implemented on the hardware. The outputted trajectory by the ANN model was not feasible or safe for hardware implementation, therefore hardware implementation of the walking trajectory was not completed. The contribution of this work is highlighted in providing a skeleton for developing a data-driven approach in order to limit the effects of structural deformation in the development of successfully walking bipeds. This work provides a detailed approach to a possible method in order to limit the effects of inaccurate kinematic modeling. Text eng https://hdl.handle.net/2142/120463 Copyright 2023 Omar Darwish M.S. Thesis Mechanical Engineering University of Illinois at Urbana-Champaign Mechanical Sci & Engineering