To solve the problem of obtaining the optimal path for mobile robots running during goal-oriented running in an unknown environment, a path planning model in biological inspired goal-oriented navigation based on Q-learning is proposed in this paper. The model includes three parts: Spatial exploration based on Q-learning, running control based on cognitive map and optimal path selection. Firstly, in space exploration, the location state is represented by place cells’ firing statues, and the state-action is learned by using dynamic ε value, which can generate cognitive map and provide the optimal path in space exploration stage. Secondly, in the running control based on cognitive map, the running direction is selected respectively according to the principle of maximum action cells’ firing and the principle of group action cells, and the multi-scale position update intervals are used to update the position. As a result, the optimal path based on different cognitive maps can be obtained. Finally, path planning’s result from space exploration stage and running control stage is compared, and the optimal path is selected. Simulation results show that the proposed model is feasible. A better path planning result can be obtained by using the dynamic ε value in space exploration. Besides, a feasible and effective path can be provided for goal-oriented running after sufficient space exploration.