YU-Bibot düzlemsel iki ayaklı robotun parametrik ayak tasarımı ve model tabanlı optimizasyonu

In this thesis, the planar bipedal robot previously developed (YU-Bibot) is simulated, and an improved parametric foot design is proposed and studied. The anatomy of the human foot is investigated to better understand the operating mechanism. The experiments which include experiments to find important parameters of the human foot are researched. Also, other robot feet are studied in the literature to examine how the problems are solved. A common tendency to reduce impact forces while walking the robots is using elastic materials under the feet. So, elastic materials are designed and printed on a 3D printer. The materials are tested to find properties such as stiffness and damping coefficients. Elastic parts are studied to implement under the foot designs. For different situations, variables of the pads are optimized for both walking and dropping (free-fall) of YU-Bibot. A new generation simulation model is designed for YU-Bibot. Real human walking cycles are studied and simulated. The simulation is used to optimize parameters. The simulation will include different gait parameters concerning different ground angles. Based on the natural characteristics of the human foot, a mechanical design that can accommodate small bumps and irregularities of the ground is proposed. Two different methods are studied. One of them is implementing elastic pads under the existing feet of YU-Bibot. And the other method is designing parametric feet. For both methods, pad specifications are optimized via a simulation model. And for the second design, dimensions are optimized via the simulation model. Optimization criteria for the designs are found and developed from previous studies of YU-Bibot. One of the past studies found the maximum torque for the capacity of the motors on the joints of the current YU-Bibot. Considering these maximum torque values, an objective function is defined. The designs are optimized by this objective function using a genetic algorithm.