Bio-Inspired Swarm Navigation on Resource-Constrained Robots Using a Dual-Modality Virtual Pheromone System with Onboard Machine Learning for GPS-Denied Environments

This project contains the code and data supporting the thesis, "Bio-Inspired Ant Foraging and Communication Mechanisms Modeling and Application to Swarm Robotics," specifically Chapter 4. The GitHub repository provides the source code, simulation scripts, and trained models that underpin the research presented in that chapter. This includes the FormicaBot's dual-modality virtual pheromone system, adaptive foraging algorithms, and the onboard machine learning pipeline for emergent role differentiation and target recognition. You can find the corresponding archived dataset on Zenodo.org, which provides a citable and stable version of the research artifacts. This combination of resources allows others to reproduce and expand upon the thesis's findings in bio-inspired swarm navigation.