Efficient Large-Scale Multi-Drone Delivery Using Transit Networks

TL;DR

This paper presents an efficient algorithmic framework for large-scale multi-drone delivery in urban areas, leveraging transit networks to optimize delivery times and energy use.

Contribution

It introduces a novel two-layer approach combining task allocation and routing with transit network integration for large drone fleets.

Findings

Framework handles up to 200 drones and 5000 packages efficiently.

Solutions are computed within seconds on standard hardware.

Drones can travel up to 360% of their flight range using transit.

Abstract

We consider the problem of controlling a large fleet of drones to deliver packages simultaneously across broad urban areas. To conserve energy, drones hop between public transit vehicles (e.g., buses and trams). We design a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery. We address the multifaceted complexity of the problem through a two-layer approach. First, the upper layer assigns drones to package delivery sequences with a near-optimal polynomial-time task allocation algorithm. Then, the lower layer executes the allocation by periodically routing the fleet over the transit network while employing efficient bounded-suboptimal multi-agent pathfinding techniques tailored to our setting. Experiments demonstrate the efficiency of our approach on settings with up to $200$ drones, $5000$ packages, and transit networks with up to $8000$ stops in San Francisco and Washington DC. Our results show that the framework computes solutions typically within a few seconds on commodity hardware, and that drones travel up to $360 \%$ of their flight range with public transit.