Author: Ahmed, Ausama Hadi
Climbing robots have the potential to be used in diverse applications, such as cleaning sky scrapers, maintaining of maritime structures, and conducting search and rescues. The focus of this thesis is on optimizing the forces of a climbing robot loitering on vertical surfaces. The optimization is primarily achieved through on minimizing the maximum normal adhesion force on the tips of the legs of a six-legged climbing robot as well as the maximum torque experienced by the joints. In this theses, the model of a six legged robot is simplified into a two dimensional structure with three legs. Furthermore, this simplified robotic model was validated by the use of biomimicry; in which the stance of the ants is analyzed using the same model and verified that their posture indeed minimizes the maximum adhesion on the tips of their legs. The optimal normal adhesion force for a climbing robot is calculated using a closed form solution. For robots with position controlled legs, the effects of different geometrical parameters and the stiffness of the materials, used to build the structure of the robot are investigated with a focus on maximum normal adhesion. Calculation of the forces on the structure uses the Finite Element Method (FEM). For robots with force/torque controlled legs, the effect of geometric parameters, specifically the height and, the length of the robot and the position of the middle leg, are also investigated with emphasis on maximum normal adhesion. The effects of the investigated parameters are summarized and presented as guidelines for the design of climbing robots. Also, the non-linear and non-differentiable problem of minimizing the maximum torque on the joints of the robot, that uses the optimal normal adhesion force on the tips of their legs, is addressed only for robots with force/torque controlled legs. Finally, a transformation that converts the problem into a linear form is presented. The proposed method was found to outperform three other widely used algorithms in terms of speed and accuracy.
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Thesis advisor: Menon, Carlo
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