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We address the problem of routing a team of drones and trucks overlarge-scale urban road networks. To conserve their limited flight energy,drones can use trucks as temporary modes of transit en route to their owndestinations. Such coordination can yield significant savings in total vehicledistance traveled, i.e., truck travel distance and drone flight distance,compared to operating drones and trucks independently. But it comes at thepotentially prohibitive computational cost of deciding which trucks and dronesshould coordinate and when and where it is most beneficial to do so. We tacklethis fundamental trade-off by decoupling our overall intractable problem intotractable sub-problems that we solve stage-wise. The first stage solves onlyfor trucks, by computing paths that make them more likely to be useful transitoptions for drones. The second stage solves only for drones, by routing themover a composite of the road network and the transit network defined by truckpaths from the first stage. We design a comprehensive algorithmic frameworkthat frames each stage as a multi-agent path-finding problem and implement twodistinct methods for solving them. We evaluate our approach on extensivesimulations with up to $100$ agents on the real-world Manhattan road networkcontaining nearly $4500$ vertices and $10000$ edges. Our framework saves onmore than $50\%$ of vehicle distance traveled compared to independently solvingfor trucks and drones, and computes solutions for all settings within $5$minutes on commodity hardware.

Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks