The cognitive model ABGP is a special model for agents,which consists of awareness,beliefs,goals and plans. The ABGP agents obtain the knowledge directly from the natural scenes only through some single preestablished rules like most agent architectures. Inspired by the biological visual cortex( V1) and the higher brain areas perceiving the visual feature,deep convolution neural networks( CNN) are introduced as a visual pathway into ABGP to build a novel visual awareness module. Then a rat-robot maze search simulation platform is constructed to validate that CNN can be used for the awareness module of ABGP. According to the simulation results,the rat-robot implemented by the ABGP with the CNN awareness module reaches the excellent performance of recognizing guideposts,which directly enhances the capability of the communication between the agent and the natural scenes and improves the ability to recognize the real world,which successfully demonstrates that an agent can independently plan its path in terms of the natural scenes. The cognitive model ABGP is a special model for agents, which consist of awareness, beliefs, goals and plans. The ABGP agents obtain the knowledge directly from the natural scenes only through some single preestablished rules like most agent architectures. (V1) and the higher brain areas perceiving the visual feature, deep convolution neural networks (CNN) are introduced as a visual pathway into ABGP to build a novel visual awareness module. Then a rat-robot maze search simulation platform is constructed to validate that CNN can be used for the awareness module of ABGP. According to the simulation results, the rat-robot implemented by the ABGP with the CNN awareness module reaches the excellent performance of recognizing guideposts, which directly enhances the capability of the communication between the agent and the natural scenes and improves the ability to recognize the real world, which successfully demonstrates that an agent can independently pla n its path in terms of the natural scenes.