Biological Inspired Goal-oriented Navigation Model Based on Spatial Exploration and Construction of Cognitive Map

To realize the generation of the navigation knowledge and the running control driven by goal for the intelligent and autonomous vehicle, a biological inspired Goal-Oriented (GO) navigation model based on spatial exploration and construction of cognitive map is discussed in this paper. This model is made up of three parts, including spatial exploration, construction of cognitive map and control of goal-oriented navigation. During spatial exploration, the model from Grid Cells (GCs) to Place Cells (PCs) and visual place cells' model are fused to represent current state, and Q-learning algorithm is used to build and update the state-action. As a result, the goal-oriented navigation knowledge is learned. Then, during the construction of cognitive map, the gravity center estimation principle is used to deal with the obtained spatial exploration knowledge, which can produce the direction information corresponding to the different place cells' state. Finally, during goal-oriented navigation process, the vehicle controls its running direction based on the cognitive map. Therefore, the goal -oriented navigation can be realized. Simulation validates that this model is available. The vehicle can construct cognitive map after sufficient spatial exploration and realizes goal-oriented navigation based on the cognitive map. Besides, the vehicle can effectively avoid obstacles during running.