2018· International Journal of Intelligent Automation & Robotics Engineering· Vol 1, pp. 01-16· 0 citations
TL;DR
Overall, intelligent robotics can significantly reduce human exposure, improve inspection quality, and enable early fault detection, while future research must focus on certifiable AI, resilient perception, and standardized benchmarking in hazardous environments.
Abstract
Industrial environments such as petrochemical plants, offshore platforms, mines, and nuclear facilities pose severe risks to human workers, including toxic exposure, explosions, radiation, and extreme conditions. To achieve safer, zero-harm operations, intelligent robotic systems are increasingly deployed for inspection, monitoring, and emergency response in hazardous or inaccessible areas. Recent advancements in multi-modal sensing, autonomous navigation, and industrial connectivity have transformed robots into capable cyber-physical agents.This paper presents an IEEE-style overview of intelligent robots in hazardous industries, structured across platform, perception, decision, communication, and assurance layers. It introduces a Risk-Aware Autonomy (RAA) framework that balances task success with safety constraints using optimization-based control. The study also highlights real-world challenges such as harsh environments, limited reliability of AI models, and certification complexities. Furthermore, the paper outlines a systematic engineering methodology covering hazard analysis, system design, validation, and operational governance. It emphasizes dual assurance—integrating safety and cybersecurity to ensure reliable deployment. Overall, intelligent robotics can significantly reduce human exposure, improve inspection quality, and enable early fault detection, while future research must focus on certifiable AI, resilient perception, and standardized benchmarking in hazardous environments.
The application of autonomous robotics in public safety is transforming how societies prepare for, respond to, and recover from emergencies. Traditional human-centered operations in disaster response, firefighting, search and rescue, law enforcement, and infrastructure protection often expose personnel to high-risk environments. Autonomous robots offer a safer alternative by operating in hazardous and uncertain conditions with minimal human intervention. Advancements in Artificial Intelligence, sensor fusion, machine perception, embedded systems, and communication technologies have enabled modern robots to make complex decisions, collaborate in teams, and adapt to dynamic environments. This paper reviews the evolution of public safety robotics, from early teleoperated systems to advanced multi-agent autonomous robots. It analyzes key technological developments, system design approaches, and operational frameworks in areas such as disaster response, autonomous surveillance, firefighting, and emergency medical support. The study proposes a staged autonomy framework incorporating perception, cognition, control, and coordination modules. Results indicate that autonomous robotics can significantly improve situational awareness, reduce response time, and enhance responder safety, while highlighting challenges related to reliability, trust, legal responsibility, and interoperability.
Hiroshi Tanaka· International Journal of Int...· 0 citations
Industrial robots underlie modern manufacturing automation, yet conventional deterministic control based on fixed trajectories and offline programming struggles under high-mix and flexible production. Embodied artificial intelligence (EAI) offers a promising alternative by coupling perception, reasoning, and action within closed-loop physical interaction, enabling industrial robots to adapt behaviors online rather than execute predefined tasks. Yet, general-purpose EAI remains difficult to deploy in industrial environments due to stringent requirements on precision, real-time performance, reliability, and safety. These challenges have motivated increasing interest in embodied artificial intelligence for industry (EAI4I). This paper presents a systematic survey of EAI4I from an industrial robotics perspective. Specifically, we first analyze the quantified requirements of industrial robots enabled by EAI4I. Afterwards, recent research progress is reviewed, covering core technologies for single- and multi-robot systems, dedicated hardware platforms, high-fidelity simulators, task-specific datasets, representative industrial application scenarios, and critical deployment challenges. Finally, promising directions toward EAI4I are discussed.
Haibin Yu, Chunhe Song, Yinlong Zhang et al.· National Science Review· 0 citations
The increasing use of Unmanned Aerial Vehicles (UAVs) in energy-sector operations- such as pipeline inspection, infrastructure monitoring, and remote asset surveillance- has highlighted critical limitations in predominantly manual and semi-automated drone systems. While current UAV deployments offer improved safety and efficiency over traditional inspection methods, their dependence on continuous human control and stable communication links restricts scalability, resilience, and operational autonomy in complex or hazardous environments. This paper presents a conceptual framework for (state) adaptive autonomous UAV systems designed to address these limitations. The proposed approach emphasizes the integration of intelligent sensing, perception, decision-making, control, and communication as coordinated layers capable of adjusting to changing operational conditions. Rather than focusing on specific implementations, the framework outlines how autonomy-driven design principles can enhance UAV reliability, reduce human intervention, and improve operational continuity in energy-sector applications. By positioning autonomy as a critical enabler rather than an optional feature, this work aligns with ongoing digital transformation and energy transition efforts. The paper discusses potential application scenarios within oil and gas, power infrastructure, and renewable energy systems, and highlights key challenges related to regulation, system validation, and future deployment. The proposed framework provides a foundation for further research and development toward resilient, intelligent UAV operations in the evolving global energy landscape.
G. I. Akanbi, O. Kolade, S. Akande et al.· SPE Nigeria Annual Internati...· 0 citations
Modern power grids require safer and more reliable field operations, yet conventional robots often face limitations in unstructured environments because of rigid pre-programming and weak perception–action coupling. This review examines Embodied Intelligence (EI) as an emerging direction for enhancing power-system field operations. We first evaluate the environmental adaptability of morphological carriers, including quadrupeds, humanoids, and unmanned aerial vehicles, and then define the perception–cognition–execution closed-loop architecture used in this review. Three application domains are then examined. Intelligent inspection focuses on active perception and potential open-vocabulary object detection. Live-line maintenance emphasizes Sim-to-Real methods and shared autonomy, while disaster-response applications involve heterogeneous air–ground robotic coordination. The review also discusses the potential for EI to reduce human exposure to hazardous tasks and influence labor structures, while a regional text-based proxy illustrates differences in policy attention to digital infrastructure. Finally, we analyze major constraints, including hardware endurance under extreme climates, edge-computing latency, foundation-model uncertainty and hallucination, cybersecurity, and safety certification. Overall, EI should not be interpreted as a mature replacement for current utility practice; it is a developing technological direction whose safe deployment will require field validation, standardized evaluation, cybersecurity assurance, and continued human supervisory authority.
Yuxin Wen, Peixiao Fan, Zhiyu Mao et al.· Applied Informatics· 0 citations
This review presents a comprehensive analysis of industrial hazard identification and automated safety control systems by synthesizing research published between 2013 and 2026, and explores the role of Safety 4.0 and Industry 5.0 paradigms in promoting human-centric, sustainable, and resilient industrial operations.
T. M. Hossain, M. Chowdhury, K. M. M. F. Mithon et al.· International Journal of Mul...· 0 citations
As the number of robots using machine-learning (ML) algorithms and connected to the Internet of Things (IoT) continues to grow in dangerous workplaces, the development and adoption of regulations and safety safeguards to ensure their use are not keeping up. Most standards on the industrial robot safety, functional safety, IoT cybersecurity and trustworthy AI stand alone, and there is no common and risk-based method to evaluate and ensure the safe use of autonomous robots learning to operate with human operators in risky environments. This study used the Design Science Research (DSR) method to design and test a risk-based regulatory and safety approach to ML-enabled IoT robots in hazardous industrial settings. The research first investigated the key safety hazards of autonomous robotic applications, such as communication failure, inaccurate ML predictions, sensor inaccuracies, unauthorised access, collision hazards, and unsafe decision making, and systematically analysed and mapped the safety requirements, regulation and technical standards relevant to industrial robots, IoT security, machine learning, functional safety, and autonomous systems to the identified hazards. In light of this analysis, a structured framework to ensure safety and compliance was designed, incorporating real-time risk assessment, ML-based threat detection, IoT security mechanisms, and pre-defined safety rules. Empirically observed results would not be reported or interpreted here, but are shown and interpreted as hypothetical, demonstrating how the accuracy of risk detection, safety compliance coverage, response time, reliability, reduction in unsafe robotic actions, and expert validation ratings would be reported if there were genuine simulated-scenario testing and expert review. The pattern showed that the proposed framework consistently attained high risk-detecting accuracy and safety compliance coverage in various simulated hazard situations and was well rated by experts on the completeness, practicability, applicability and regulatory fit.
Unknown authors· Journal of Global Social Tra...· 0 citations