Swarm Robots Localization

This project explores localization strategies for robotic swarms, focusing on how distributed agents can estimate their own positions through limited information exchange. Using hop-count localization and the simulated annealing algorithm, the system refines each robot’s estimated coordinates based on a small set of known “seed” robot positions. By minimizing a global cost function iteratively, the algorithm improves positional accuracy and stability within the network. The project demonstrates how optimization and probabilistic modeling can enhance decentralized spatial awareness—an essential step toward scalable, self-organizing robotic systems for exploration and collective intelligence.