Bing Zhenshan, Sewisy Amir Ei, Zhuang Genghang, Walter Florian, Morin Fabrice O, Huang Kai, Knoll Alois
IEEE Trans Neural Netw Learn Syst. 2022 May;33(5):2147-2158. doi: 10.1109/TNNLS.2021.3128380. Epub 2022 May 2.
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.
作为动物至关重要的认知功能,导航技能首先建立在对环境中方向朝向的准确感知之上。在动物边缘系统中发现的头部方向细胞(HDCs),被证明在水平面上以自我中心的方式识别方向朝向方面发挥着重要作用,与动物的位置和环境的周围条件无关。然而,很少有人研究能够在机器人应用中实现的实用HDC模型,特别是那些具有生物学合理性且适用于现实世界的模型。在本文中,我们提出了一种计算HDC网络,该网络与关于生物HDCs的几个神经生理学发现一致,然后将其应用于机器人导航任务中。HDC网络仅依靠角速度作为输入来保持方向朝向的表征。我们在广泛的模拟和实际实验中检验了所提出的HDC模型,并展示了其在准确性和实时能力方面的优异性能。