Methods and algorithms for motion control of walking mobile robot with obstacle avoidance

Abstract

In this paper we intend to find a control method for an autonomous hexapod walking robot. The proposed algorithm transforms the captured image into a binary one, which, after partitioning, is analyzed by a neural network. The imposed trajectory is changing, depending on the optimum angle of rotation to avoid the obstacle. This angle represents the neural network's output. Also we want to find the optimal solution for moving a leg (the leg has three degrees of freedom). This movement has to be with less energy consumption. To achieve this, we started from inverse kinematics and we apply a genetic algorithm. For determining the fitness function values we consider the importance of three factors (Precision, Movement and Friction) on each sequence of step. To find the coefficients which determine the order of these factors we use the Analytical Hierarchy Process.