[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83585-en":3,"doc-seo-83585-105":29,"detail-sidebar-cat-0-en-105":91},{"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},83585,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","The Three Dimensions of ROS 2 Middleware","ROS 2 has become the de facto standard for modern robot software, with middleware such as Data Distribution Service (DDS) and Zenoh enabling distributed robotic communication. Despite architectural flexibility, these systems show structural limitations in dynamic, wireless, and resource-constrained deployments. The paper surveys ROS 2 middleware and proposes a framework based on three structural dimensions: Space, Time, and State. It analyzes how trade-offs among discovery, data exchange, and state management affect scalability, responsiveness, and robustness under real-world conditions.","The Three Dimensions of ROS 2 Middleware  \nSanghoon Lee, Taehun Kim, Angelo Corsaro and Kyung-Joon Park  \narXiv :2607 .0 1304v 1 [ cs .RO] 1 Jul 2026  \nAbstract—ROS 2 (Robot Operating System 2) has emerged asthe de facto standard for modern robot software development, with middleware implementations such as the Data Distribution Service (DDS) and Zenoh forming the core infrastructure for distributed robotic communication. Despite their architectural flexibility, these middleware systems exhibit structural limitations, particularly under dynamic and resource-constrained wireless environments. This paper presents a systematic survey of ROS 2 middleware and introduces a conceptual framework to examine its architectural limits through three structural dimensions required by distributed robotic systems, namely Space, Time, and State. We first provide a structured analysis of middleware architecture and operational dynamics, including discovery, data exchange, and state management mechanisms. Building on this foundation, we formalize Time as temporal predictability for control loops, Space as spatial abstraction from physical topology to enable modular deployment, and State as contextual continuity despite dynamic node participation and intermittent connectivity. Through a comprehensive review of existing implementations and prior studies, we organize middleware research according to the structural tradeoffs that arise among these dimensions. Under constrained wireless conditions, spatial abstraction can obscure network variability and weaken temporal guarantees, while mechanisms that preserve state continuity introduce computational and network overhead that competes with time-critical communication. These interactions reveal structural trade-offs that characterize the practical limits of contemporary robot middleware. By synthesizing architectural patterns and identifying gaps in current modeling and analysis approaches, this survey outlines a principled research roadmap for robust and scalable robotic middleware architectures.  \nIndex Terms—Robot Operating System 2 (ROS 2), middleware, Data Distribution Service (DDS), Zenoh, Distributed robotic systems, Survey, Communication Architecture  \nI. INTRODUCTION  \nRobot Operating System 2 (ROS 2) has emerged as the de facto standard for modern robot software development [1], [2] . The growing distribution and modularization of robotic systems has driven this transition, as perception, planning, and control components are deployed across multiple processes, devices, and network domains. As robotic platforms expand toward multi-robot coordination, edge-cloud integration, and wireless deployment, communication increasingly spans heterogeneous and resource-constrained environments [3]–[5] . Within ROS 2, the middleware layer abstracts and manages this complex communication through implementations such as Data Distribution Service (DDS) and Zenoh [6] . These middleware systems constitute the core infrastructure for discovery, data exchange, and state management, thereby playing a central architectural role in determining system scalability, responsiveness, and robustness [7], [8] .  \nSanghoon Lee, Taehun Kim, and Kyung-Joon Park are with the Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, South Korea. (e-mail: [leesh2913@dgist.ac.kr](leesh2913@dgist.ac.kr), [taehun@dgist.ac.kr](taehun@dgist.ac.kr), [kjp@dgist.ac.kr](kjp@dgist.ac.kr)).  \nAngelo Corsaro is with Eclipse Zenoh. ([e-mail:ac@zenoh.io](e-mail:ac@zenoh.io)).  \nDistributed robotic systems simultaneously require temporal predictability for control loops, spatial abstraction from physical topology to enable modular and scalable deployment, and sustained state continuity despite dynamic node participation and intermittent connectivity. When these requirements are exercised in real deployments, distributed systems frequently exhibit performance degradation [9], [10] . In wireless settings, bandwidth fluctuations","cbCaidW6Pgs45qGG","https://ap.wps.com/l/cbCaidW6Pgs45qGG","pdf",1236075,1,31,"English","en",105,"# Introduction\n## ROS 2 middleware role in distributed robotics\n## Motivation: performance degradation in real deployments\n## Research gap in middleware-layer design","[{\"question\":\"What three structural dimensions does the paper use to analyze ROS 2 middleware?\",\"answer\":\"The framework defines Time, Space, and State. Time captures temporal predictability for control loops, Space provides spatial abstraction from physical topology, and State ensures contextual continuity despite dynamic nodes and intermittent connectivity.\"},{\"question\":\"Why do ROS 2 middleware systems face challenges in dynamic wireless environments?\",\"answer\":\"Wireless bandwidth and latency variability weaken temporal predictability, while changing network topology can delay or disrupt discovery and synchronization. State-preserving mechanisms also add computational and network overhead that can interfere with time-critical communication.\"},{\"question\":\"What research gap does the paper identify in existing ROS 2 literature?\",\"answer\":\"Only a small fraction of ROS 2 publications explicitly investigate middleware-layer design and analysis, and existing studies often lack a unified architectural framework that captures cross-dimensional interactions among Time, Space, and State requirements.\"}]",1784189010,78,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":27},"the-three-dimensions-of-ros-2-middleware","",{"@graph":35,"@context":85},[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/the-three-dimensions-of-ros-2-middleware/83585/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What three structural dimensions does the paper use to analyze ROS 2 middleware?","Question",{"text":75,"@type":76},"The framework defines Time, Space, and State. Time captures temporal predictability for control loops, Space provides spatial abstraction from physical topology, and State ensures contextual continuity despite dynamic nodes and intermittent connectivity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do ROS 2 middleware systems face challenges in dynamic wireless environments?",{"text":80,"@type":76},"Wireless bandwidth and latency variability weaken temporal predictability, while changing network topology can delay or disrupt discovery and synchronization. State-preserving mechanisms also add computational and network overhead that can interfere with time-critical communication.",{"name":82,"@type":73,"acceptedAnswer":83},"What research gap does the paper identify in existing ROS 2 literature?",{"text":84,"@type":76},"Only a small fraction of ROS 2 publications explicitly investigate middleware-layer design and analysis, and existing studies often lack a unified architectural framework that captures cross-dimensional interactions among Time, Space, and State requirements.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]