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.

自主导航是机器人技术的一个基本问题。传统方法一般在感知空间中构建点云图或密集特征图；由于缺乏认知和记忆形成机制，传统方法存在鲁棒性差、认知能力低等问题。仿生导航方法作为一种从哺乳动物导航中汲取灵感的新型导航技术，能够将感知信息映射到认知空间，具有很强的自主性和环境适应性。为了提高机器人自主导航能力，提出一种基于认知地图的分层导航方法。首先，对哺乳动物的导航相关网格单元和头部方向单元进行建模，为机器人提供位置认知。然后提出一种基于认知图的全局路径规划策略，能够高效、短距离地预测一条到达目标的首选全局路径。此外，提出了一种分层运动控制方法，将目标导航分为多个子目标导航，移动机器人能够以高置信度到达这些子目标。最后进行了一些实验，结果表明，所提出的路径规划方法能够避免穿越障碍物，高效地获得一条到达目标的优选全局路径，并且时间成本并没有随着经验节点数量的增加而急剧增加。运动控制结果表明，移动机器人仅依靠自身运动信息就能成功到达目标，是一种有效的尝试，体现了较强的仿生特性。