Hierarchical privacy focused routing improves autonomous delivery in buildings
Hierarchical Building-Floo-Room Framework for Privacy-Preserving Autonomous Delivery in Large-Scale Buildings
Robotics
Summary
Large buildings can be tricky for autonomous delivery robots because they have many floors and rooms, and some areas need privacy. The authors created a system that plans routes by thinking about the building, floor, and room levels separately. This approach helps robots deliver items efficiently while respecting privacy rules. They also built and tested a robot system that follows these plans reliably in real buildings.
What this means in practice
- •For facilities management teams: Coordinate autonomous delivery robots in large buildings with privacy constraints and multiple floors for efficient scheduling and routing.
- •For robotics platform developers: Build robotic delivery platforms that integrate privacy preserving hardware designs with hierarchical mapping and navigation systems validated in real-world buildings.
Authors
Xu Xiaoji, Luo Rui, Chen Jinhua, Xiang Changying, Zhang Leihan, Liao Zhongyuan
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.