학술논문

Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network
Document Type
Periodical
Source
IEEE Transactions on Neural Networks and Learning Systems IEEE Trans. Neural Netw. Learning Syst. Neural Networks and Learning Systems, IEEE Transactions on. 33(5):2147-2158 May, 2022
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
General Topics for Engineers
Neurons
Biological system modeling
Computer architecture
Microprocessors
Navigation
Computational modeling
Turning
Autonomous driving
cognitive navigation
continuous attractor network (CAN)
head direction cells (HDCs)
neural simultaneous localization and mapping (SLAM)
Language
ISSN
2162-237X
2162-2388
Abstract
As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an important role in identifying the directional heading allocentrically in the horizontal plane, independent of the animal’s location and the ambient conditions of the environment. However, practical HDC models that can be implemented in robotic applications are rarely investigated, especially those that are biologically plausible and yet applicable to the real world. In this article, we propose a computational HDC network that is consistent with several neurophysiological findings concerning biological HDCs and then implement it in robotic navigation tasks. The HDC network keeps a representation of the directional heading only relying on the angular velocity as an input. We examine the proposed HDC model in extensive simulations and real-world experiments and demonstrate its excellent performance in terms of accuracy and real-time capability.