ROS-Based Control Framework

• ROS-based control framework is a modular software architecture that integrates perception, planning, and actuation across diverse robotic systems.
• It leverages middleware integration and distributed node communication to support scalable, extensible, and interoperable multi-robot applications.
• Emerging techniques incorporate declarative task planning, skill-based behaviors, and optimization methods to ensure real-time performance, safety, and adaptability.

A ROS-based control framework is a software architecture built on top of the Robot Operating System (ROS) that coordinates perception, planning, and actuation in robotic and automation systems. These frameworks provide well-defined abstractions, middleware integration, and modularity for building scalable, extensible, and high-performance robotics applications. The following sections synthesize foundational and contemporary approaches for ROS-based control frameworks drawn from academic literature and advanced engineering practice.

1. Architectural Foundations and Middleware Integration

ROS-based control frameworks universally leverage ROS as the backbone for message-passing, distributed computation, and hardware abstraction. The architectural stack typically comprises:

• Core Coordination Layer: Implements high-level reasoning, task planning, or sequencing (e.g., Answer Set Programming integration in ROSoClingo (Andres et al., 2013), or hierarchical scheduling via Extended Finite Automata in Sequence Planner (Dahl et al., 2019)).
• Middleware and Communication Interface: ROS nodes facilitate event-driven or periodic communication via topics, services, and actions. Notably, in large-scale industrial applications, ROS 2 introduces the Data Distribution Service (DDS) for peer-to-peer, decentralized discovery, enhanced real-time guarantees, and resilience (see (Erős et al., 2019)).
• Device and Actuation Abstraction: Hardware interface nodes translate between abstract task representations and device-specific control interfaces. This modularity enables rapid hardware changes and simulation-to-hardware portability, as seen in NimbRo-OP (Allgeuer et al., 2018) and scalable frameworks for multi-robot systems (Salimi et al., 2023).

The integration of external reasoning engines (e.g., oClingo for reactive ASP (Andres et al., 2013)), hybrid bridges (ROS1<->ROS2 via static/dynamic bridges (Erős et al., 2019)), and support for distributed or reconfigurable hardware (ReconROS for FPGAs (Lienen et al., 2021)) further characterizes the evolution of ROS-based control frameworks.

2. Task Planning, Sequencing, and High-Level Reasoning

Modern frameworks emphasize high-level declarative task planning, dynamic reactivity to environment changes, and seamless goal dispatch:

• Declarative Planning: ROSoClingo encodes robot behavior declaratively in Answer Set Programs (ASP), converting high-level goals and feedback into ASP fact streams for reasoning and plan synthesis. This approach partitions knowledge into base, incremental, and volatile components enabling cycle-based updates and commitment to past actions (Andres et al., 2013).
• Extended Finite Automata and Hierarchies: Sequence Planner models abilities as EFAs with precise transition guards, action functions, and formal safety constraints; high-level production operations are mapped to temporal logic goals and realized through bounded model checking (Dahl et al., 2019).
• Skill-Based Abstractions and Behavior Trees: SkiROS2 formulates control in terms of modular skills, encompassing pre-, hold-, and post-conditions, scheduled by an extended Behavior Tree (eBT) that merges symbolic planning (PDDL-driven) with reactive execution (Mayr et al., 2023).

This layering permits both dynamic re-planning in response to sensor feedback (e.g., blocked paths in mail delivery tasks (Andres et al., 2013)) and specification of complex mission constraints and optimization criteria.

3. Handling Heterogeneous and Distributed Systems

ROS-based frameworks are architected for extensibility, interoperability, and distributed execution:

• Transformation Pipelines: State-of-the-art frameworks employ pipelines to discretize, transform, and synchronize messages between device drivers and control logic, supporting both ROS2-native and ROS1-legacy components (Sequence Planner Layer 0 (Dahl et al., 2019)).
• Communication Bridges: ROS2 communication architectures utilize static bridges for reliable, direction-filtered topic and service relaying, mitigating inconsistency risks inherent to dynamic topic discovery and supporting mixed ROS1/ROS2 deployments (Erős et al., 2019).
• Modular Node Structures: Modular separation of perception, estimation, control, and behavioral layers (e.g., NimbRo-OP (Allgeuer et al., 2018), Socially-interactive Robot Software (Akhyani et al., 2023)) underpins scalability and ease of debugging across hardware and simulation.

Distributed multi-agent frameworks (e.g., ChoiRbot (Testa et al., 2020), CrazyChoir (Pichierri et al., 2023)) exploit ROS/ROS2's decentralized messaging and the DDS middleware to implement distributed optimization, formation control, and task assignment algorithms, demonstrated in realistic collaborative and industrial scenarios.

4. Integration of Advanced Control, Optimization, and Learning

Control frameworks are increasingly integrating advanced optimization and learning approaches:

• Distributed Optimal Control: Solutions like ChoiRbot support distributed MPC and task assignment formulated as networked optimization problems, leveraging peer-to-peer ROS2 message exchange and asynchrony robust communication classes (Testa et al., 2020).
• Active Learning and ML Model Deployment: AWML is an open-source framework for ML-driven perception tightly coupled with ROS 2, supporting MLOps workflows—model exporting, active learning, and semantic versioning—to deploy models (e.g., CenterPoint, BEVFusion, YOLOX_opt) in real-time autonomy stacks such as Autoware (Tanaka et al., 31 May 2025).
• Imitation and Human-in-the-Loop Learning: The ROS-LLM framework (Mower et al., 28 Jun 2024) integrates LLMs for natural-language robot programming, supporting task decomposition into sequences, behavior trees, or state machines, while incorporating imitation learning and feedback-driven policy correction.

Frameworks for social and collaborative robotics (e.g., SROS (Akhyani et al., 2023), SkiROS2 (Mayr et al., 2023)) expose perception, speech, and decision modules as ROS services, facilitating rapid adaptation and the coordinated integration of new capabilities.

5. Real-Time Guarantees, Safety, and Industrial Application

Industrial viability of ROS-based frameworks is predicated on robust real-time guarantees, access control, and safety features:

• Real-Time Executor Design: Micro-ROS supports resource-constrained hardware by redesigning the executor to exploit reservation-based sporadic scheduling (e.g., NuttX RTOS), assigning per-thread execution time budgets and period, thus ensuring deadlines are met or exceeding workloads are demoted in priority (Staschulat et al., 2021).
• Attribute-Based Secure Control: Multi-robot systems employ attribute-based access control (ABAC) frameworks tied to a permissioned blockchain (Hyperledger Fabric) for secure, auditable command execution and real-time conflict resolution based on dynamic user/robot attributes and task priorities (Salimi et al., 2023).
• Unified Modeling for Correctness: The MeROS metamodel (Winiarski, 2023) leverages SysML, providing a platform-independent, structured representation of both ROS 1 and 2 systems (nodes, nodelets, packages, communication) for error reduction, standardization, and system-level validation.

Combined, these strategies ensure safety, extensibility, and traceability in both research and deployed industrial robotics settings.

6. Emerging Directions and Open Challenges

Recent frameworks reveal several trajectories for ongoing research and application advancement:

• Unified Human-Robot-Multi-Agent Coordination: Modular ROS-based control infrastructures for monitoring and synchronizing human and robot operational conditions leverage timestamped message filters, feature extraction modules, and scalable multi-robot support to enable adaptive collaboration in safety-critical domains (Jo et al., 2020).
• Customizable, Reconfigurable User Interfaces: The emergence of web-based, component-driven GUIs (using ROSBridge and roslibjs) for intuitive system management and dynamic feature integration lowers the entry barrier and supports industrial-scale adoption (Fresnillo et al., 4 Jun 2024).
• Hardware Acceleration and Energy Efficiency: ReconROS (Lienen et al., 2021) demonstrates seamless task offloading between CPU and FPGA-based ROS nodes, with API and memory abstractions to facilitate high-throughput, low-latency pipeline integration.

Key open challenges remain in scalable cross-platform modeling, declarative to geometric co-planning, interface abstraction for heterogeneous hardware, continuous real-time adaptation, and balancing transparency with ease of use for non-expert stakeholders.

• ROSoClingo: A ROS package for ASP-based robot control (Andres et al., 2013)
• A ROS-based Software Framework for the NimbRo-OP Humanoid Open Platform (Allgeuer et al., 2018)
• Sequence Planner - Automated Planning and Control for ROS2-based Collaborative and Intelligent Automation Systems (Dahl et al., 2019)
• A ROS2 based communication architecture for control in collaborative and intelligent automation systems (Erős et al., 2019)
• A ROS-based Framework for Monitoring Human and Robot Conditions in a Human-Multi-robot Team (Jo et al., 2020)
• ChoiRbot: A ROS 2 Toolbox for Cooperative Robotics (Testa et al., 2020)
• Budget-based real-time Executor for Micro-ROS (Staschulat et al., 2021)
• Design of Distributed Reconfigurable Robotics Systems with ReconROS (Lienen et al., 2021)
• ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction (Mower et al., 2022)
• ROS-Based Multi-Agent Systems COntrol Simulation Testbed (MASCOT) (Pandit et al., 2022)
• CrazyChoir: Flying Swarms of Crazyflie Quadrotors in ROS 2 (Pichierri et al., 2023)
• MeROS: SysML-based Metamodel for ROS-based Systems (Winiarski, 2023)
• Project-Based Learning for Robot Control Theory: A Robot Operating System (ROS) Based Approach (Farzan, 2023)
• SkiROS2: A skill-based Robot Control Platform for ROS (Mayr et al., 2023)
• A Customizable Conflict Resolution and Attribute-Based Access Control Framework for Multi-Robot Systems (Salimi et al., 2023)
• Modular Customizable ROS-Based Framework for Rapid Development of Social Robots (Akhyani et al., 2023)
• An Open and Reconfigurable User Interface to Manage Complex ROS-based Robotic Systems (Fresnillo et al., 4 Jun 2024)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning (Mower et al., 28 Jun 2024)
• AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software (Tanaka et al., 31 May 2025)