Safe-ROS: An Architecture for Autonomous Robots in Safety-Critical Domains
- URL: http://arxiv.org/abs/2511.14433v1
- Date: Tue, 18 Nov 2025 12:34:33 GMT
- Title: Safe-ROS: An Architecture for Autonomous Robots in Safety-Critical Domains
- Authors: Diana C. Benjumea, Marie Farrell, Louise A. Dennis,
- Abstract summary: We contribute the Safe-ROS architecture for developing reliable and verifiable autonomous robots in safety-critical domains.<n>We demonstrate Safe-ROS on an AgileX Scout Mini robot performing autonomous inspection in a nuclear environment.<n>Our results demonstrate that the Safe-ROS architecture can provide safety verifiable oversight while deploying autonomous robots in safety-critical domains.
- Score: 1.491109220586182
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deploying autonomous robots in safety-critical domains requires architectures that ensure operational effectiveness and safety compliance. In this paper, we contribute the Safe-ROS architecture for developing reliable and verifiable autonomous robots in such domains. It features two distinct subsystems: (1) an intelligent control system that is responsible for normal/routine operations, and (2) a Safety System consisting of Safety Instrumented Functions (SIFs) that provide formally verifiable independent oversight. We demonstrate Safe-ROS on an AgileX Scout Mini robot performing autonomous inspection in a nuclear environment. One safety requirement is selected and instantiated as a SIF. To support verification, we implement the SIF as a cognitive agent, programmed to stop the robot whenever it detects that it is too close to an obstacle. We verify that the agent meets the safety requirement and integrate it into the autonomous inspection. This integration is also verified, and the full deployment is validated in a Gazebo simulation, and lab testing. We evaluate this architecture in the context of the UK nuclear sector, where safety and regulation are crucial aspects of deployment. Success criteria include the development of a formal property from the safety requirement, implementation, and verification of the SIF, and the integration of the SIF into the operational robotic autonomous system. Our results demonstrate that the Safe-ROS architecture can provide safety verifiable oversight while deploying autonomous robots in safety-critical domains, offering a robust framework that can be extended to additional requirements and various applications.
Related papers
- Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories [18.300558114535992]
Self-Driving Laboratories (SDLs) create closed-loop experimental systems capable of autonomous hypothesis generation, experimentation, and analysis.<n>This paper presents Safe-SDL, a comprehensive framework for establishing robust safety boundaries and control mechanisms.
arXiv Detail & Related papers (2026-02-13T12:42:48Z) - RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic [56.38397499463889]
Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks.<n>However, they remain vulnerable to hazardous instructions that may trigger unsafe behaviors.<n>We propose RoboSafe, a runtime safeguard for embodied agents through executable predicate-based safety logic.
arXiv Detail & Related papers (2025-12-24T15:01:26Z) - OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
arXiv Detail & Related papers (2025-10-28T13:22:39Z) - Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems [2.28126966226433]
Demonstrating the safety of autonomous systems rigorously is critical for their responsible adoption.<n>OOD detection is receiving increased attention from the research, development and safety engineering communities.<n>This comprehensive review analyses OOD detection techniques within the context of safety assurance for autonomous systems.
arXiv Detail & Related papers (2025-10-24T08:38:01Z) - ANNIE: Be Careful of Your Robots [48.89876809734855]
We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
arXiv Detail & Related papers (2025-09-03T15:00:28Z) - A Verification Methodology for Safety Assurance of Robotic Autonomous Systems [0.1962656580942496]
This paper presents a verification workflow for the safety assurance of an autonomous agricultural robot.<n>It covers the entire development life-cycle, from concept study and design to runtime verification.<n>Results show that the methodology can be effectively used to verify safety-critical properties and facilitate the early identification of design issues.
arXiv Detail & Related papers (2025-06-24T13:39:51Z) - Towards provable probabilistic safety for scalable embodied AI systems [79.31011047593492]
Embodied AI systems are increasingly prevalent across various applications.<n> Ensuring their safety in complex operating environments remains a major challenge.<n>This Perspective offers a pathway toward safer, large-scale adoption of embodied AI systems in safety-critical applications.
arXiv Detail & Related papers (2025-06-05T15:46:25Z) - SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
arXiv Detail & Related papers (2025-05-23T10:56:06Z) - Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models [0.0]
This paper presents an enterprise-level, risk-aware, security-by-design approach for large-scale autonomous AI systems.<n>We detail a unified pipeline that delivers provable guarantees of model behavior under adversarial and operational stress.<n>Case studies in national security, open-source model governance, and industrial automation demonstrate measurable reductions in vulnerability and compliance overhead.
arXiv Detail & Related papers (2025-05-09T20:14:53Z) - Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
arXiv Detail & Related papers (2025-04-30T13:47:25Z) - Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy [53.048430683355804]
Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks.<n>We present Safety-as-policy, which includes (i) a world model to automatically generate scenarios containing safety risks and conduct virtual interactions, and (ii) a mental model to infer consequences with reflections.<n>We show that Safety-as-policy can avoid risks and efficiently complete tasks in both synthetic dataset and real-world experiments.
arXiv Detail & Related papers (2024-11-27T12:27:50Z) - Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs [12.787160626087744]
We propose a novel integration of Large Language Models with Embodied Robotic Control Prompts (ERCPs) and Embodied Knowledge Graphs (EKGs)
ERCPs are designed as predefined instructions that ensure LLMs generate safe and precise responses.
EKGs provide a comprehensive knowledge base ensuring that the actions of the robot are continuously aligned with safety protocols.
arXiv Detail & Related papers (2024-05-28T05:50:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.