Hierarchical Building-Floo-Room Framework for Privacy-Preserving Autonomous Delivery in Large-Scale Buildings

Abstract

Autonomous delivery in large scale buildings requires coordinated planning across multiple spatial levels, including building, floor, and room, while ensuring scheduling efficiency. Existing methods often focus solely on algorithmic development, neglecting integration with physical robotic platforms, and rarely validate performance in real world scenarios. Many approaches also overlook hierarchical building structures and room level privacy constraints, limiting practical applicability. To address these challenges, this paper presents a hierarchical delivery framework based on a Building-Floor-Room abstraction, which decomposes the problem across multiple levels to significantly reduce optimization complexity. We formulate a building aware extension of the capacitated vehicle routing problem (CVRP) that explicitly models inter building and inter floor connectivity, room level access constraints, and privacy sensitive areas, enabling globally feasible and privacy compliant task assignment and routing. Based on this hierarchical model, the framework can evaluate delivery efficiency across different building layouts and optimize depot placement for specific configurations. At the execution level, we introduce a novel hardware design for non interactive, privacy preserving delivery, supported by reachability aware distances and a closed loop mapping, localization, and navigation system, ensuring consistency between planned routes and physical navigation. The proposed framework is validated on a real multi sensor robotic platform, demonstrating reliable multi floor delivery with improved path rationality and overall efficiency.