Robotic Navigation Based on Experiences and Predictive Map Inspired by Spatial Cognition

Humans and animals have environmental cognition and navigation abilities. These abilities are closely related to the spatial cognitive mechanism of brain. Based on this mechanism, we propose a novel robotic navigation framework based on experiences and predictive map inspired by spatial cognition to accurately construct environment experience and quickly plan path. The grid cell and the place cell are modeled to rapidly integrate self-motion cues. The multidimensional grid coding and one-shot learning rule are utilized for activating the place representation of robot pose. Visual cues provide information for relocation. These information are integrated through experiences which express the topology of the environment, enabling the robot to accurately achieve spatial cognition of complex environment. In order to realize the sequential decision making of hippocampus, the predictive map is introduced to quickly plan the experience sequence to target in dynamic environment. The successor representation model of robot's state is constructed through reinforcement learning. Combined with the goal-based reward function, the shortest path can be replanned to adapt to environment changes. The proposed method is tested in simulated maze, Kitti dataset and corridor environment. Compared with other bionic navigation methods, it has a faster computing speed and higher precision with bionic characteristics. Compared with visual navigation methods, the navigation tasks can robustly be accomplished without complex system design.