robot systems integration

This study addresses the challenge of simultaneously achieving spatial abstraction, temporal predictability, and state continuity in ROS 2 middleware operating within dynamic, resource-constrained wireless environments. For the first time, it introduces a three-dimensional “space–time–state” analytical framework, integrating architectural analysis, formal modeling, and a comprehensive literature review to systematically investigate the underlying mechanisms and structural trade-offs of ROS 2 middleware—particularly DDS and Zenoh—in discovery protocols, data exchange, and state management. The work uncovers critical performance bottlenecks in existing approaches concerning modular deployment, real-time control, and disconnected operation recovery. These insights lay a theoretical foundation and offer concrete design guidelines for developing robust, scalable next-generation robotic middleware.

To address the challenges of ROS 2—including reliance on local deployment, poor reproducibility, high educational barriers, and significant security risks—this paper introduces ROS2Wasm, the first native browser-based ROS 2 execution framework. Leveraging WebAssembly cross-compilation and RoboStack containerization, ROS2Wasm enables zero-installation, in-browser execution of the ROS 2 core, RoboStack ecosystem, and the Robotics Toolbox for Python. A lightweight communication middleware—built upon WebSockets and SharedArrayBuffer—supports real-time TF visualization, sensor simulation, and Python script execution. Furthermore, integration with the Swift Web simulator and PyTorch/Open3D Web bindings ensures safe, sandboxed execution with strict isolation guarantees. Deployed at ros2wasm.dev, the system achieves startup latency under 500 ms and supports all major browsers. ROS2Wasm significantly enhances reproducibility in robotics research, accessibility in education, and runtime security—all without compromising functionality or performance.

This work addresses the limitations of existing robotic middleware—such as ROS 2—whose architectural complexity, poor interoperability, and steep learning curve hinder the development of efficient cross-platform robotic systems. To overcome these challenges, we propose Meta-ROS, a lightweight middleware architecture built upon modern communication protocols like Zenoh and ZeroMQ. Meta-ROS streamlines integration, supports multimodal data transmission—including audio and video—and enables seamless cross-platform deployment. Experimental results demonstrate that Meta-ROS achieves up to 30% higher throughput, lower message latency, and reduced resource consumption compared to ROS 2, thereby providing a highly compatible, low-latency foundation for real-time robotic AI applications.

In ROS-based robotic system development, model-based systems engineering (MBSE) remains disconnected from practical engineering practice, and there is a lack of structured, safety-critical development paradigms. Method: This paper proposes MeROS—a V-model-driven metamodel method specifically tailored for ROS/ROS 2—systematically extending SysML to support domain-specific, tool-agnostic, customizable, and reusable development workflows spanning requirements analysis, architectural design, implementation, verification, and bidirectional traceability. Contribution/Results: Integrated with an MBSE toolchain and empirically validated on the HeROS heterogeneous multi-robot platform, MeROS significantly enhances bidirectional traceability among requirements, design models, and source code, while improving overall system consistency. It establishes a novel development paradigm for safety-critical robotic systems that balances regulatory compliance, engineering rigor, and practical flexibility.

To address challenges in the Internet of Robotic Things (IoRT)—including limited onboard perception, insufficient environmental semantic understanding, and poor real-time performance in dynamic obstacle avoidance—this paper proposes a ROS-based collaborative architecture integrating intelligent spaces with mobile robots. Methodologically, it introduces a distributed environmental perception network as an external sensing enhancement module within ROS, enabling closed-loop coordination among multi-source sensor fusion, semantic environmental modeling, and autonomous robot decision-making. Additionally, a lightweight real-time collision prediction algorithm is designed to reduce reliance on individual robot intelligence. Experimental results demonstrate significant improvements in complex dynamic environments: obstacle avoidance success rate increases by 23.6%, and system response latency decreases by 41%. These findings validate the effectiveness and scalability of the proposed IoRT architecture for cost-constrained robotic systems.

This work proposes a lightweight and scalable multi-robot orchestration framework based on ROS 2 to address the challenges of flexible configuration, rapid reconfiguration, and efficient coordination in high-mix, low-volume manufacturing environments. The framework encapsulates robot functionalities as deployable skills and leverages Compute Continuum principles to automatically construct isolated execution units, dynamically instantiate skill deployments, and enable resource-aware communication coordination. Experimental results demonstrate that the system significantly reduces CPU, memory, and network overhead in idle states, outperforming K3s-based solutions in energy efficiency and overall performance, thereby making it well-suited for large-scale edge deployment scenarios.

This work addresses the challenge of recovering the hierarchical software architecture of ROS 2 systems, which is implicitly embedded in distributed artifacts and difficult to reconstruct explicitly. Existing approaches lack support for decomposing and composing structures across multiple abstraction levels. To overcome this limitation, the paper proposes an agent-based, multi-level architecture recovery method that integrates blueprint-guided reasoning with large language models (LLMs). By analyzing launch file dependencies and parsing node lists, the approach leverages multi-granular intermediate architectural representations and refined prompting strategies to iteratively reconstruct cross-layer structural constraints. Experimental evaluation on a real-world collaborative robotic disassembly system demonstrates that the method significantly improves structural consistency, scalability, and robustness in architecture recovery, while also uncovering critical challenges related to dynamic semantic integration in large-scale systems.

This work addresses the limited generalizability and transferability of current foundation models in robotics, which often require custom integration of perception, actuation, and safety mechanisms. To overcome this, the authors propose ROSClaw—a model-agnostic execution layer that enables plug-and-play deployment of arbitrary foundation models on any ROS 2 robot by integrating the OpenClaw agent runtime with ROS 2. Key innovations include standardized capability discovery, multimodal observation normalization, action validation within configurable safety bounds, and structured audit logging. Experiments across three robotic platforms and four foundation models demonstrate up to a 4.8× difference in non-policy action proposal rates and show that the proposed execution layer significantly improves task success rates and safety across diverse frameworks.

This work addresses the challenges of dependency isolation, compatibility, reproducibility, and hardware resource sharing in multi-user collaborative and heterogeneous robotic deployments. To this end, it proposes a containerized architecture tailored for robot teams operating within edge–cloud协同 environments. The architecture uniquely integrates system-level containers (LXC/LXD), ROS 2/DDS communication middleware, and a three-tier edge infrastructure—comprising infrastructure core, platform orchestration, and compute acceleration—to enable topology-aware networking, strong isolation, and controllable resource sharing. Experimental validation in a real-world robotic laboratory demonstrates that the proposed approach significantly simplifies software integration, improves resource utilization, and supports secure prototyping alongside reproducible collaborative experimentation.

This work addresses the limitations of ROS launch files, which, being text-based, struggle to clearly represent inter-module connections and interfaces, often leading to configuration errors and poor readability. To overcome these challenges, the authors propose a model-driven, visual modeling approach based on WebGME, implementing a dedicated environment called ROSLaunchVisual. This framework enables graphical design of nodes, topic-based communication, and parameter configurations, while supporting semantic validation, automated import/export of launch files, and accurate communication mapping. The proposed method significantly enhances the intuitiveness of launch file development, improves system comprehensibility, and facilitates collaborative efficiency, thereby reducing misconfiguration rates. It is particularly well-suited for the efficient development and maintenance of large-scale or collaboratively engineered robotic systems.