Building an Egocentric-to-Allocentric Travelling Direction Transformation Model for Enhanced Navigation in Intelligent Agents - Zugang Chen; Haodong Wang (Sensors)
Many behavioral tasks in intelligent agent research involve working with mathematical vectors. While traditional methods perform well in some cases, they struggle in complex and dynamic environments. Recently, bionic neural networks have emerged as a novel solution. Studies on the Drosophila central complex have revealed that these insects use neural signals from the ellipsoid body and fan to track allocentric travel angles and update spatial awareness during movement, a process that heavily relies on directional vector manipulation. Our model accurately replicates the neural connectivity of the Drosophila central complex, drawing inspiration from the half-adder unit to efficiently encode and process spatial direction information. This framework significantly enhances the accuracy of coordinate transformations while increasing adaptability and resilience in challenging environments. Our experimental results demonstrate that the bionic neural network outperforms traditional methods, delivering superior precision and robust generalizability within the coordinate system.