[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82549-en":3,"doc-seo-82549-105":29,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82549,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","From Real Time Planning to Reliable Execution Scalable Coordination for Heterogeneous Multi Robot Fleets in Industrial Environments","Efficient coordination for heterogeneous multi-robot fleets in industrial environments remains challenging as real-time path planning must handle dense robot populations and differing motion capabilities. Communication delays, execution uncertainties, and external disturbances can push robots off the timing assumptions of planned paths, causing excessive waiting and congestion propagation. The paper proposes SCALE, a reactive online coordination framework that enables real-time planning with robust execution, including motion-induced conflict reduction and a generalized CAPH structure for adaptive precedence under disturbances. Validation and a three-day warehouse deployment demonstrate feasibility and effectiveness.","From Real-Time Planning to Reliable Execution: Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments  \nBo Cao  \narXiv :2607 .0059 1v2 [ cs .RO] 3 Jul 2026  \nAbstract—With the increasing deployment of heterogeneous robot fleets in industrial environments, efficient coordination remains a critical challenge. Real-time path planning must simultaneously accommodate high robot densities and heterogeneous motion capabilities, while communication delays, execution uncertainties, and other disturbances may cause robots to deviate from the temporal assumptions underlying planned paths. Such deviationscan lead to excessive waiting and congestion propagation across the fleet. This paper presents SCALE, a reactive online coordination framework that enables real-time planning while maintaining robust execution. Within this framework, we introduce a motion-induced conflict reduction mechanism to support the online generation of feasible paths for online conflict resolution. To mitigate the effects of disturbances, we further design a generalized Conjugate Action-Precedence Hypergraph (CAPH) that adaptively adjusts precedence relations among robots. Extensive validation experiments, together with a three-day deployment in a warehouse, demonstrate the practical feasibility and effectiveness of the proposed method.  \nIndex Terms—Heterogeneous multi-robot systems, multi-robot path planning (MRPP), robust execution, planning and improving while executing  \nI. INTRODUCTION  \nINTELLIGENT manufacturing systems and warehousing  \nlogistics are increasingly operated by unmanned robot fleets, including automated guided vehicles (AGVs) such as Kiva-like robots, autonomous forklifts, mobile manipulators, and large stacker robots. Compared with homogeneous mobile-robot teams, such fleets differ substantially in footprint and kinematic behavior. Coordinating them in dense workspaces is therefore not merely a path-planning problem:  \nthe system must generate feasible routes in real time, avoid collisions and deadlocks in limited space, and maintain robust execution despite communication latency, tracking errors, and uncertain waiting caused by humans or third-party facilities. In today’s industrial multi-robot deployments, coordination is commonly built on well-designed topological roadmaps or traffic zones. Collision avoidance and deadlock prevention are typically implemented by engineering-oriented methods such as zone control [1], Petri-net supervisors [2], gluednode reservation [3], or other handcrafted traffic rules. These methods are useful in specific scenarios because their safety logic is explicit and easy to certify. However, when dozens of tasks are involved, scheduling can take tens of seconds, which is not efficient enough for industrial systems. More  \nimportantly, for large-scale robot fleet, such strategies tend to be conservative: zones, guidepath segments, or reserved vertices often have to be allocated according to worst-case occupancy, reducing space utilization and degrading system efficiency in high-density scenarios [3] . Their performance also strongly depends on manually designed layouts and rules, which severely limits their generality.  \nRecent advances in multi-agent path finding (MAPF) offer the possibility of more general planning. Representative algorithms, such as MAPF-LNS2 [4], LaCAM, and its improved variant LaCAM* [5], [6], have demonstrated strong scalability and solution quality in MAPF benchmarks. Nevertheless, most MAPF solvers rely on a graph-agent abstraction, in which each agent occupies a vertex or traverses an edge at a discrete time step. This creates a substantial gap when applied to realworld heterogeneous robots: the occupied space of a robot is determined not only by its vertex position, but also by its rigid-body footprint, orientation, and kinematic constraints. Although some approaches [7], [8] partially relax the pointagent assumption, they still struggle to preserve the re","cbCaipGgpfzCgiJr","https://ap.wps.com/l/cbCaipGgpfzCgiJr","pdf",22934614,1,11,"English","en",105,"# Introduction\n## Industrial multi-robot coordination challenges\n## Limits of zone-control and topology-based methods\n## MAPF background and gaps for heterogeneous robots\n## Robust MAPF under disturbances\n# SCALE framework\n## Reactive planning-execution architecture\n## Motion-induced conflict reduction\n## Conjugate Action-Precedence Hypergraph (CAPH)\n# Validation and real-world deployment","[{\"question\":\"Why can deviations from planned timing lead to fleet congestion in industrial multi-robot systems?\",\"answer\":\"External disturbances such as communication delays and execution uncertainties can cause robots to deviate from the temporal assumptions behind planned paths. This can create excessive waiting and propagate congestion across the fleet.\"},{\"question\":\"What is SCALE and what problem does it target?\",\"answer\":\"SCALE is a reactive online coordination framework for large-scale heterogeneous robot fleets in industrial environments. It integrates real-time planning with robust execution under disturbances to maintain collision- and deadlock-free operation.\"},{\"question\":\"How does SCALE generate feasible paths for online conflict resolution?\",\"answer\":\"SCALE groups potentially conflicting robots and uses a motion-induced conflict reduction mechanism to support the online generation of feasible, kinematically compatible paths for resolving conflicts during execution.\"},{\"question\":\"How does CAPH help with latency-resilient asynchronous execution?\",\"answer\":\"SCALE designs a generalized Conjugate Action-Precedence Hypergraph (CAPH) that adaptively adjusts precedence relations among robots. This mitigates disturbances by improving robustness of asynchronous execution despite communication latency and timing deviations.\"}]",1784181469,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":27},"from-real-time-planning-to-reliable-execution-scalable-coordination-for-heterogeneous-multi-robot-fleets-in-industrial-environments","",{"@graph":35,"@context":89},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/from-real-time-planning-to-reliable-execution-scalable-coordination-for-heterogeneous-multi-robot-fleets-in-industrial-environments/82549/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why can deviations from planned timing lead to fleet congestion in industrial multi-robot systems?","Question",{"text":75,"@type":76},"External disturbances such as communication delays and execution uncertainties can cause robots to deviate from the temporal assumptions behind planned paths. This can create excessive waiting and propagate congestion across the fleet.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is SCALE and what problem does it target?",{"text":80,"@type":76},"SCALE is a reactive online coordination framework for large-scale heterogeneous robot fleets in industrial environments. It integrates real-time planning with robust execution under disturbances to maintain collision- and deadlock-free operation.",{"name":82,"@type":73,"acceptedAnswer":83},"How does SCALE generate feasible paths for online conflict resolution?",{"text":84,"@type":76},"SCALE groups potentially conflicting robots and uses a motion-induced conflict reduction mechanism to support the online generation of feasible, kinematically compatible paths for resolving conflicts during execution.",{"name":86,"@type":73,"acceptedAnswer":87},"How does CAPH help with latency-resilient asynchronous execution?",{"text":88,"@type":76},"SCALE designs a generalized Conjugate Action-Precedence Hypergraph (CAPH) that adaptively adjusts precedence relations among robots. 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