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The method that agricultural mobile robot acquire the navigation strategies through autonomous learning was development based on reinforcement learning and fuzzy logic. Firstly, the machine vision was applied to detect obstacles in the navigation environment, and the corresponding direction and distance between the robot and the obstacle was calculated. Then the algorithm of acquiring the more optimal navigation strategies was introduced with the reinforcement learning, so the capability of the mobile robot of adapting the dynamic navigation environment was improved. Finally, the continuous values of the direction and the distance between the obstacles and the mobile robot were discretized with the fuzzy logic rules, and the discrete navigation environment states were obtained, then the Q value table was designed for the reinforcement learning. The experiment was carried out with the wheeled mobile robot, and the experimental results showed that the mobile robot was able to automatically acquire more optimal navigation strategies in the actual environment, and fulfill the expected navigation tasks.