Geospatial brain-inspired navigation: a neurocognitive approach for autonomous systems in complex environments - Donghui Han; Tong Qin; Tianyu Yang; Hua Liao; Qiaosong Hei; Fangwen Yu; Bailu Si; Weihua Dong (Geo-spatial Information Science)
Autonomous navigation plays a crucial role in cutting-edge scientific and technological domains, such as autonomous driving and space exploration. Current models often rely on knowledge of the discharge patterns of navigation cells in living organisms (e.g. place/grid cells) to encode spatial information, which works well in ideal environments. However, real-world autonomous navigation presents greater challenges due to complex and dynamic geospatial information, leading to issues such as low robustness, poor interpretability, and high energy consumption for existing models. To address these challenges, it is essential to explore the roles and functional connectivity of distinct brain regions involved in processing real-world geospatial information and integrate these insights into autonomous navigation algorithms. This paper reviews empirical studies using neuroscientific techniques to investigate how the human brain processes geographical information during navigation. In particular, we discuss opportunities and challenges associated with three critical aspects: (1) expanding the understanding of cognitive mechanisms from isolated regional functions to integrated functional connectivity and large-scale brain networks, (2) refining neurocognitive experiments to provide ecologically valid evidence in complex and dynamic contexts and (3) developing efficient approaches to computationally mimic and implement spatial cognition mechanisms of human brain in navigation algorithms. Addressing these difficulties would not only enable machines to navigate autonomously and effectively in complex real-world and extreme environments (e.g. space and the deep sea) but also pave the way for the development of future intelligent systems (e.g. GeoAI) with human-like cognitive capabilities.