Applied Neural Cross-Correlation into the Curved Trajectory Detection Process for Braitenberg Vehicles

TL;DR

This paper presents a neural cross-correlation implementation for curved trajectory detection in cognitive agents, enhancing accuracy and scalability in robotic navigation through a biologically inspired circuit model.

Contribution

It introduces a hard-wired neural cross-correlation circuit integrated into a scalable CTD system, improving high-level trajectory detection in robotic applications.

Findings

The circuit effectively detects curved trajectories with high precision.

Simulation results demonstrate improved navigation accuracy in a PIONEER robot model.

The approach offers scalable integration into neuronal networks for cognitive robotics.

Abstract

Curved Trajectory Detection (CTD) process could be considered among high-level planned capabilities for cognitive agents, has which been acquired under aegis of embedded artificial spiking neuronal circuits. In this paper, hard-wired implementation of the cross-correlation, as the most common comparison-driven scheme for both natural and artificial bionic constructions named Depth Detection Module(DDM), has been taken into account. It is manifestation of efficient handling upon epileptic seizures due to application of both excitatory and inhibitory connections within the circuit structure. Presented traditional analytic approach of the cross-correlation computation with regard to our neural mapping technique and the acquired traced precision have been turned into account for coherent accomplishments of the aforementioned design in perspective of the desired accuracy upon high-level cognitive reactions. Furthermore, the proposed circuit could be fitted into the scalable neuronal network of the CTD, properly. Simulated denouements have been captured based on the computational model of PIONEER mobile robot to verify characteristics of the module, in detail.