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
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.
Table 2: Intra-building delivery routes with capacity Q=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=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=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=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.
Strategy
Stage 1 Cost
Stage 2 Cost
Total Cost
Δ (%)
Random Baseline
3200.0
1580.0
4780.0
–
Sequential Baseline
1800.0
784.0
2584.0
−45.9%
Hybrid (Opt S1 + Rand S2)
1700.0
1160.0
2860.0
−40.2%
Hierarchical CVRP
1700.0
668.0
2368.0
−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} . (b) Stage 2: single-building environment with Q∈{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) cases.
Figure 21: Iteration histories under uniform inter-building distances.
Figure 22: Representative real-world deployment of the autonomous delivery system
The development of trajectory planning strategies for autonomous ground vehicles (AGVs) represents a prevailing research interest within the domain of intelligent transportation systems. This paper introduces a trajectory planning framework tailored for multi-floor scenarios. The framework consists of two main modules: the task planning module and the trajectory planning module. The task planning module involves a strategic selection phase, where a task planning strategy based on generalized voronoi diagrams (GVD) and multi-objective algorithms is proposed to select the floor exits for each floor. The trajectory planning module utilizes optimization-based methods to generate high-quality trajectories, and a warm-started hierarchical planning framework is designed to ensure rapid convergence. Additionally, for handling complex obstacle constraints, a correlation constraint calculation method is designed for reducing obstacle constraints in trajectory planning. Finally, the feasibility and effectiveness of the proposed framework are verified through simulations.
Zishang Xiang, Runda Zhang, Runqi Chai +3
school of Automation, Beijing Institute of Technology, Beijing, China
In the vision of smart cities, technologies are being developed to enhance the efficiency of urban services and improve residents' quality of life. However, most existing research focuses on optimizing individual services in isolation, without adequately considering reciprocal interactions among heterogeneous urban services that could yield higher efficiency and improved resource utilization. For example, human couriers could collect traffic and air quality data along their delivery routes, while sensing robots could assist with on-demand delivery during peak hours, enhancing both sensing coverage and delivery efficiency. However, the joint optimization of different urban services is challenging due to potentially conflicting objectives and the need for real-time coordination in dynamic environments. In this paper, we propose UrbanHuRo, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing. UrbanHuRo includes two key designs: (i) a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch, and (ii) a deep submodular reward reinforcement learning algorithm for sensing route planning. Experimental evaluations on real-world datasets from a food delivery platform demonstrate that UrbanHuRo improves sensing coverage by 29.7% and courier income by 39.2% on average in most settings, while also significantly reducing the number of overdue orders.
Tonmoy Dey, Lin Jiang, Zheng Dong +1
Department of Computer Science, Florida State University. · Department of Computer Science, Wayne State University.
Space-efficient warehouse layouts often contain single-agent-width aisles and dead-end workstations where robots have few places to wait without blocking others. In Multi-Agent Pickup and Delivery (MAPD) on such constrained layouts, robots must accept online pickup-delivery tasks while preserving protected waiting locations called Havens. The Safe HAven Retreat Planner (SHARP) introduced a mechanism that extends each committed task path with a validated retreat to the agent's dedicated initial Haven, but fixed-Haven commitments can send agents toward distant Havens after deliveries. We present A-sharp (Adaptive SHARP), which changes an agent's retreat target at task assignment time. A naive switch can cause two agents to rely on the same waiting location or let another committed path pass through a location that is still occupied or reserved. A-sharp prevents these failures with an availability test for candidate Havens and a pending-release rule that keeps the previous Haven protected until the agent departs. Under explicit Haven-structure and Safe Interval Path Planning (SIPP) assumptions, we prove invariant preservation and finite-release completeness: every task in any finite release sequence is delivered in finite time. Across 72,000 runs on 14,400 paired map-agent-count-rate-seed cases over four maps, both SHARP and A-sharp complete their respective 14,400 runs. For makespan (final delivery time), a prespecified paired comparison with Holm correction over all 138 configurations with more Havens than agents finds A-sharp significantly better in 107 configurations and never significantly worse than SHARP; on the tested tree map, the median reduction is 16.7%.
Taisei Hirayama, Kohei Yoshida, Hiroki Sakaji +1
Hokkaido University, Sapporo 060-0808, Japan · Toyota Industries Corporation, Aichi 474-8601, Japan