Energy Efficient Collaborative SLAM Frameworks for Swarm Robotics

Abstract

Collaborative SLAM frameworks are essential for enabling autonomous swarm robotics to navigate complex and dynamic environments efficiently. This study proposes an energyefficient SLAM architecture that integrates lightweight feature extraction, distributed mapping, and next-generation wireless communication to optimize both computational and communication resources. Each robotic agent performs local mapping and pose estimation while sharing critical information with peers over ultra-low latency networks, enabling cooperative loop closure detection and consistent global map construction. Swarm intelligence principles guide decentralized coordination, ensuring robustness, scalability, and fault tolerance, even in resourceconstrained environments. Experimental evaluations demonstrate reduced energy consumption, improved trajectory accuracy, and enhanced system scalability compared to traditional centralized SLAM approaches, confirming the framework’s suitability for next-generation autonomous swarm deployments.