Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile Robots

Autonomous navigation is a fundamental problem in robotics. Traditional methods generally build point cloud map or dense feature map in perceptual space; due to lack of cognition and memory formation mechanism, traditional methods exist poor robustness and low cognitive ability. As a new navigation technology that draws inspiration from mammal's navigation, bionic navigation method can map perceptual information into cognitive space, and have strong autonomy and environment adaptability. To improve the robot's autonomous navigation ability, this paper proposes a cognitive map-based hierarchical navigation method. First, the mammals' navigation-related grid cells and head direction cells are modeled to provide the robots with location cognition. And then a global path planning strategy based on cognitive map is proposed, which can anticipate one preferred global path to the target with high efficiency and short distance. Moreover, a hierarchical motion controlling method is proposed, with which the target navigation can be divided into several sub-target navigation, and the mobile robot can reach to these sub-targets with high confidence level. Finally, some experiments are implemented, the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency, and the time cost does not increase extremely with the increase of experience nodes number. The motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information, which is an effective attempt and reflects strong bionic properties.

Automated summary

This paper proposes a cognitive map-based hierarchical navigation method to improve the robot's autonomous navigation ability. The method draws inspiration from mammal's navigation, mapping perceptual information into cognitive space and exhibiting strong autonomy and environment adaptability. Experimental results demonstrate that the proposed path planning method can efficiently avoid obstacles and find the preferred global path to the target, while the motion controlling results reflect strong bionic properties.