Architectural Synergy in Distributed Robotic Networks: Integrating Resilient Data Versioning, Neuro-Evolutionary Explainable AI, And Quantum-Classical Hybrid Optimization for Autonomous Swarm Coordination

Abstract

This research investigates the convergence of distributed computing frameworks, advanced machine learning versioning, and autonomous robotic coordination. As multi-agent systems transition from centralized control to decentralized, large-scale deployments, the necessity for robust data management and explainable decision-making becomes paramount. This paper synthesizes the foundational principles of MapReduce and Resilient Distributed Datasets (RDD) with modern neuro-evolutionary approaches to Explainable AI (XAI) and hybrid quantum-classical machine learning models. By examining the impact of data versioning on model reliability and the role of scalable leader selection algorithms, the study proposes a comprehensive framework for managing "swarm intelligence" in complex environments. The analysis extends to the kinematic constraints of non-holonomic robots, distributed receding horizon control, and the "piano movers" problem, providing a theoretical bridge between high-level data processing and low-level motion planning. The results demonstrate that integrating data versioning protocols significantly enhances the reproducibility of autonomous behaviors, while neuro-evolutionary strategies provide the necessary transparency for human-robot interaction in critical sectors like e-healthcare. The conclusion outlines a future where quantum-enhanced distributed systems provide the computational power required for real-time, finite-time stabilization of multi-agent networks.