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Movement Planning of a Hexapod Robot
Hostačná, Kristína ; Šátek, Václav (oponent) ; Rozman, Jaroslav (vedoucí práce)
This thesis presents neural network solution for achieving autonomous navigation to seen target in hexapod robots. This solition was implemented on two designs - a simple design with two joints per leg and a more complex design with three joints per leg. Major challenge of hexapod locomotion - the complexity of controlling more joints was therefore increased to enable a wider range of motions and therefore uses for the robot. Research methodology involves both a simulation and a real-world experimentation, for modeling and data collection. Data from various sensors, including cameras and servo positions, are utilized to train neural network models capable of interpreting sensory inputs and generating control signals for the robot's actuators. A basic neural network architecture is initially deployed for both configurations, while a more sophisticated approach incorporating convolutional and recurrent neural networks is employed later. Ultimately, the trained neural networks demonstrate capability of navigation to target.

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