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Drones may temporarily ride on trucks to conserve limited flight energy, reducing total vehicle distance relative to independent operation. The main challenge is the high computational cost of selecting coordinating vehicles and deciding when and where coordination helps. The approach decomposes the global problem into stage-wise multi-agent path-finding subproblems: first computing truck transit utility, then routing drones over an expanded road-plus-transit network. Extensive simulations on the Manhattan network with up to 100 agents show savings exceeding 50% in traveled distance, with solutions found within 5 minutes on commodity hardware.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/coordinated-multi-agent-pathfinding-for-drones-and-trucks-over-road-networks/505053/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/coordinated-multi-agent-pathfinding-for-drones-and-trucks-over-road-networks/505053.png","ImageObject",300,407,{"name":92,"@type":93},"McGucket","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-16","2026-10-04",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How do drones coordinate with trucks in the proposed routing framework?","Question",{"text":112,"@type":113},"Drones can use trucks as temporary transit to reach their destinations. Coordination aims to reduce both truck travel distance and drone flight distance compared with independent routing.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why is the original coordinated routing problem computationally difficult?",{"text":117,"@type":113},"It requires deciding which drones and trucks should coordinate and when/where coordination is beneficial, which leads to an intractable global optimization problem.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the core idea of the solution approach?",{"text":121,"@type":113},"The method decouples the overall problem into two tractable multi-agent path-finding stages: first routing trucks to maximize their usefulness as transit, then routing drones over a composite network built from the road graph and the truck-defined transit network.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},505053,1791652386,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},1236954412713,"https://us-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks  \nShushman Choudhury Stanford University, USA [shushman@cs.stanford.edu](shushman@cs.stanford.edu)  \nKiril Solovey  \nTechnion, Israel Inst. of Technology [kirilsol@stanford.edu](kirilsol@stanford.edu)  \nMykel Kochenderfer  \nStanford University, USA [mykel@stanford.edu](mykel@stanford.edu)  \nMarco Pavone  \nStanford University, USA [pavone@stanford.edu](pavone@stanford.edu)  \nABSTRACT  \nWe address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., truck travel distance and drone flight distance, compared to operating drones and trucks independently. But it comes at the potentially prohibitive computational cost of deciding which trucks and drones should coordinate and when and where it is most beneficial to do so. We tackle this fundamental trade-off by decoupling our overall intractable problem into tractable sub-problems that we solve stage-wise. The first stage solves only for trucks, by computing paths that make them more likely to be useful transit options for drones. The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage. We design a comprehensive algorithmic framework that frames each stage as a multi-agent path-finding problem and implement two distinct methods for solving them. We evaluate our approach on extensive simulations with up to 100 agents on the real-world Manhattan road network containing nearly 4500 vertices and 10000 edges. Our framework saves on more than 50% of vehicle distance traveled compared to independently solving for trucks and drones, and computes solutions for all settings within 5 minutes on commodity hardware.  \nKEYWORDS  \nMulti-Agent Path Finding; Transit Planning; Routing  \nACM Reference Format:  \nShushman Choudhury, KirilSolovey, Mykel Kochenderfer, and Marco Pavone.  \n2022. Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks. In Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), Online, May 9–13, 2022, IFAAMAS, 9 pages.  \n1 INTRODUCTION  \nDrones have great potential for transforming urban logistics services. By enabling quick, flexible, and efficient delivery, they can help address the rapidly growing logistics and e-commerce needs of dense urban populations [13] . They can also reduce our reliance on traditional ground delivery services that contribute to traffic  \nProc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), P. Faliszewski, V. Mascardi, C. Pelachaud, M.E. Taylor (eds.), May 9–13, 2022, Online. © 2022 International Foundation for Autonomous Agents and Multiagent Systems ([www.ifaamas.org](www.ifaamas.org)). All rights reserved.  \ncongestion [20] . However, operating package delivery services that rely solely on drones may be infeasible due to their limited flight range and carrying capacity [31] . To overcome these limitations, we study the problem of operating delivery drones in tandem with ground vehicles by allowing drones to ride on ground vehicles to conserve energy and increase effective flight range [10, 12] .  \nIn particular, we focus on routing a team of drones and trucks over a common road network, where drones can use trucks as transit in addition to flying. We frame our setting as a coordinated extension of the Multi-Agent Path Finding (MAPF) problem [30], which requires us to compute start-goal routes for all agents while aiming to minimize the total path cost incurred by the drones and trucks. The path cost can be different for the two agent types and can encode the energy consumed or the operational expense. As in","cbCaina2TwfmBjGv","https://ap.wps.com/l/cbCaina2TwfmBjGv","pdf",1172696,"English","# Abstract\n# Introduction\n## Problem framing as coordinated MAPF\n## Contributions and staged solution overview","[{\"question\":\"How do drones coordinate with trucks in the proposed routing framework?\",\"answer\":\"Drones can use trucks as temporary transit to reach their destinations. Coordination aims to reduce both truck travel distance and drone flight distance compared with independent routing.\"},{\"question\":\"Why is the original coordinated routing problem computationally difficult?\",\"answer\":\"It requires deciding which drones and trucks should coordinate and when/where coordination is beneficial, which leads to an intractable global optimization problem.\"},{\"question\":\"What is the core idea of the solution approach?\",\"answer\":\"The method decouples the overall problem into two tractable multi-agent path-finding stages: first routing trucks to maximize their usefulness as transit, then routing drones over a composite network built from the road graph and the truck-defined transit network.\"}]","Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks | PDF",1791087477,23]