Robot navigation map construction method based on rat brain hippocampus spatial cells

Abstract

The invention provides a robot navigation map construction method based on rat brain hippocampus spatial cells. According to an information transfer loop of spatial navigation related cells in a hippocampus structure of a mammal, a robot acquires a current autonomic movement clue and color depth image information through environment exploration. The autonomic movement clue progressively forms coding to the spatial environment through path integration and characteristic extraction of the spatial cells in the hippocampus structure. The place field of place cells is gradually formed in an exploration process and furthermore covers the whole spatial environment, thereby forming a cognitive map. Meanwhile, Kinect acquires color depth image information of a view scene right in front of the current position as an absolute reference for performing closed-loop detection on the path and correcting a path integration error. At a closed-loop point, a system resets spatial cell discharging activity and performs correction on the path integration error. Nodes on a final navigation map comprise place cell mass coding information, corresponding visual cues and place topological relationships.

Description

technical field [0001] The invention relates to a method for constructing a robot navigation map based on the hippocampus space cells of the mouse brain. According to the characteristics of spatial cells with navigation-related characteristics in the mammalian hippocampus, a method for constructing autonomous navigation maps for autonomous mobile robots is developed. Navigation map construction for robots in multi-scale and complex environments. Background technique [0002] Most of the environmental map construction methods at the present stage are based on SLAM methods, such as grid map and topological map method, based on wide-angle cameras to extract environmental feature points to construct maps, and have proposed a lot of visual signal and image processing algorithms and hardware performance. High requirements, while the division of grids and the recruitment of topological points are mostly artificial settings, which can only be oriented to specific static environment...

AI Technical Summary

Problems solved by technology

[0002] Most of the environmental map construction methods at the present stage are based on SLAM methods, such as grid map and topological map method, based on wide-angle cameras to extract environmental feature points to construct maps, and have proposed a lot of visual signal and image processing algorithms and hardware performance. High requirements, while the division of grids and the recruitment of topological points are mostly artificial settings, which can only be oriented to specific static environments, and the degree of scalability is not high

With the increasing complexity of the current sports environment, especially in a dynamic environment, a single navigation method can no longer meet the actual needs. The multi-navigation strategy can complement the advantages of several navigation methods to achieve better results. The commonly used data fusion method is Kalman filter algorithm, however, the application of Kalman filter algorithm needs to accurately construct the motion model and observation model of the system, and the modeling of complex dynamic environments requires complex calculations, which limits the application of Kalman filter

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