cs.ROOct 15, 2026

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

Authors: Xu Xiaoji, Luo Rui, Chen Jinhua, Xiang Changying, Zhang Leihan, Liao Zhongyuan

Organizations: Division of Integrative Systems and Design, Hong Kong University of Science and Technology, Hong Kong, China · Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Hong Kong University of Science and Technology, Hong Kong, China

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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.

Figures & tables

Figure 1: Hierarchical delivery framework. Modules include perception and mapping, reachable distance matrix construction, building- and privacy-aware task scheduling, subgoal decomposition, and local navigation with feedback.
Figure 2: Hierarchical Building Representation
Figure 3: Hardware configuration of the autonomous delivery robot.
Figure 4: Parcel auto delivery mechanism at the final stage.
Figure 5: Overall architecture of the unified mapping and navigation system.
Figure 6: Mapping pipeline. Raw 3D points are projected into a top-view occupancy and cost map, which is further abstracted into a topological graph used by the CVRP-based planner.
Figure 7: Representative residential building layout typologies.
Figure 8: Hierarchical spatial representation of the residential delivery scenario.
Route Pattern (with Demand)CountTotal DemandDistance Cost
[0, 1 (4), 0]14200
[0, 1 (1), 3 (3), 0]14300
[0, 2 (4), 0]54200
[0, 3 (4), 0]24200
[0, 4 (4), 0]34200
[0, 5 (4), 0]34200
Table 1: Building-level optimized delivery routes (capacity Q=4Q=4 ).
BldgTotal DemandOmni-direction Routes with DemandLoad per TripDistance Cost
15[4.5 (1), 1.3 (3)] [2.4 (1)][4,1]132.0
220[4.5 (1), 4.4 (1), 4.3 (1), 4.1 (1)] [3.2 (1), 3.3 (1), 5.1 (2)] [2.4 (1), 2.2 (2), 1.1 (1)] [1.3 (3), 1.5 (1)] [5.3 (3), 5.5 (1)][4,4,4,4,4]334.0
311[2.5 (2), 2.4 (2)] [1.4 (2), 3.3 (1), 3.2 (1)] [5.3 (1), 5.4 (1), 4.1 (1)][4,4,3]232.0
414[4.3 (2), 5.5 (1), 5.3 (1)] [3.4 (2), 3.5 (2)] [4.5 (1), 2.3 (2), 2.5 (1)] [1.2 (1), 1.4 (1)][4,4,4,2]280.0
513[2.1 (1), 5.4 (1), 5.2 (2)] [2.4 (1), 4.1 (1), 4.4 (2)] [3.3 (1), 3.1 (2), 1.1 (1)] [1.3 (1)][4,4,4,1]282.0
Table 2: Intra-building delivery routes with capacity Q=4Q=4 , with total demand separated into a dedicated column.
Figure 9: Hierarchical optimized delivery route.
Figure 10: Representative variations of inter-building spatial distributions. All scenarios share a single depot (red) but differ in the number and placement of buildings: (a) 5 buildings, (b) 10 buildings, (c) 30 buildings. Each node is annotated with its corresponding delivery demand.
Figure 11: Floor-level structural variations used to evaluate the planner’s sensitivity to vertical architectural depth. (a) Single floor, (b) 5 floors, and (c) 10 floors. All floors share identical intra-floor layouts, with a total demand of 12 packages and capacity Q=4Q=4 , producing three optimal trips for all cases.
Figure 12: Intra-floor geometric configurations illustrating differences in room density. (a) A single-room layout; (b) a 10-room layout. Both scenarios contain 12 total packages and capacity Q=4Q=4 , resulting in three optimal trips.
Figure 13: Classification of Residential Building Layouts Based on Branching Configurations.
Figure 14: Canonical corridor-topology abstractions used to assess the structural generality of the scheduling algorithm. (a) One-branch layout, (b) loop layout, (c) two-branch layout (3–2 separation), and (d) three-branch layout (2–1–2 separation). All scenarios contain 12 packages and capacity Q=4Q=4 , yielding three optimal delivery trips.
Figure 15: Inter-building delivery loops for three strategies: (a) Random Baseline; (b) Sequential Baseline; (c) Hierarchical CVRP Model.
StrategyStage 1 CostStage 2 CostTotal CostΔ\Delta (%)
Random Baseline3200.01580.04780.0–
Sequential Baseline1800.0784.02584.0−45.9%-45.9\%
Hybrid (Opt S1 + Rand S2)1700.01160.02860.0−40.2%-40.2\%
Hierarchical CVRP1700.0668.02368.0−50.5%\mathbf{-50.5\%}
Table 3: Comparison of delivery strategies in terms of travel cost
Figure 16: Comparison of elevator cost modeling strategies: (a) no elevator modeling, (b) waiting-time–only modeling, (c) floor-distance–only modeling, and (d) the proposed combined model.
Figure 17: Impact of vehicle capacity on scheduling performance. (a) Stage 1: 5-building environment with Q∈{1,5,10}Q\in\{1,5,10\} . (b) Stage 2: single-building environment with Q∈{1,5,10}Q\in\{1,5,10\} .
Figure 18: Effect of privacy-aware routing on Room-Level navigation. Without privacy penalties, the planner generates paths that enter sensitive residential areas. With privacy-aware cost inflation, such intrusions are avoided. Red line denote privacy-penalized edges.
Figure 19: Comparison of scheduling outcomes with and without privacy-aware constraints. Left: without privacy penalties; Right: with privacy-aware penalties. Red regions represent privacy-sensitive areas.
Figure 20: Iteration histories of upper and lower bounds under heterogeneous inter-building distances for the four (B,D)(B,D) cases.
Figure 21: Iteration histories under uniform inter-building distances.
Figure 22: Representative real-world deployment of the autonomous delivery system
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