Cognitive Map Building Research Articles

A neurobiologically inspired mapping and navigating framework for mobile robots

To improve the cognitive ability of the mobile robot in an unknown environment, this paper proposes an episodic memory based self-organizing learning (EM-SOL) framework for robotic experiences learning, cognitive map building and navigation. This EM-SOL framework uses the spatial cells in the hippocampus and entorhinal cortex to path integrate the robotic kinesthetic cues for dead reckoning, and meanwhile extracts the visual cues to activate state neurons for state recognition. By updating the state neurons network, this framework constructs the robotic episodic memory to store these particular experiences created along the exploration process, and achieves the robotic self-organizing learning. The framework based map building method can optimize the cumulative error by introducing the spatial cells phase reset mechanism, and build an episodic-cognitive map that describes not only the topology relationship but also the cognition relationship between these particular experiences in the environment. Furthermore, a combined topological and metric vector navigation method is presented, which can locate the robot relative to its previous experiences in the memory space, anticipate a preferred global path to the target, and guide the mobile robot to navigate to the target accurately. Finally, the proposed EM-SOL framework based methods are evaluated in both the physical environments and the standard KITTI dataset. The results show that the mobile robot can keep on learning to adapt to the changes in an unknown environment, construct valid episodic memory to store these particular learned experiences, build an episodic-cognitive map and execute the target navigation task with high efficiency and accuracy.

North in the head: spatial reference frame and map orientation

North in the head: spatial reference frame and map orientation

Automatic Fuzzy Cognitive Map Building Online System

Under the present-day global crisis, global economy regionalization and technological areas redistribution there has been seen an unprecedented uncertainty growth along with various risks, which makes it harder to take managing decisions. The scenario approach appears to be one of the most popular when it comes to modelling weakly-structured subject fields and complex problems. Fuzzy cognitive maps have good prospects in the field, as they enable us to describe both the structure and the dynamic of the area under study. This paper goes along the topical automated data science and focuses on developing the OCAM (Online Cognitive Automated Mapper) system that allows to automatically-build cognitive maps without turning to experts. The cognitive map building data source of this system is website logs. The article features the main algorithms, the system architecture and some of the system work results.

The effect of learning by imitation on a multi-robot system based on the coupling of low-level imitation strategy and online learning for cognitive map building

It is assumed that future robots must coexist with human beings and behave as their companions. Consequently, the complexities of their tasks would increase. To cope with these complexities, scientists are inclined to adopt the anatomical functions of the brain for the mapping and the navigation in the field of robotics. While admitting the continuous works in improving the brain models and the cognitive mapping for robots’ navigation, we show, in this paper, that learning by imitation leads to a positive effect not only in human behavior but also in the behavior of a multi-robot system. We present the interest of low-level imitation strategy at individual and social levels in the case of robots. Particularly, we show that adding a simple imitation capability to the brain model for building a cognitive map improves the ability of individual cognitive map building and boosts sharing information in an unknown environment. Taking into account the notion of imitative behavior, we also show that the individual discoveries (i.e. goals) could have an effect at the social level and therefore inducing the learning of new behaviors at the individual level. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low-level imitation strategy in the multi-robot system.