Systems-Level Planning and Coordination of Truck-Drone Collaborative Delivery Networks

Abstract:Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-to-end system efficiency under operational constraints. Results show a 42.4% reduction in total delivery time and a 44.2% reduction in energy consumption compared to a conventional truck-only delivery model. The scalability analysis further highlights how coordination gains persist as system size increases, and shows the importance of efficient control and communication in heterogeneous delivery networks.

Networking and Internet Architecture (cs.NI); Robotics (cs.RO)

arXiv:2606.08738 [cs.NI]

(or arXiv:2606.08738v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2606.08738 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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