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Robot locomotion control passes through a series of sensors that, according to information from the environment, allow the robot to adapt, in real time, its locomotion scheme or trajectory. When the goal of the robot is to reach a target in a non-structured environment the best approach is visual control realized by a fast image processing system. Fast parallel image processing of the CNN-UM cP4000 chip prototype permits one to obtain good performance, even in a real time control problem. The robot controlled by the implemented CNN visual feedback has a hexapod configuration and its locomotion system is also implemented by a multi-layer CNN structure. In this paper a CNN approach for both locomotion generation and visual control of the bio-inspired robot is presented.