Aug 2026· Applied Informatics· 0 citations· 124 references
Abstract
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.
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
It is argued that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms.
Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal et al.· 0 citations
This paper analyzes key technological developments, system design approaches, and operational frameworks in areas such as disaster response, autonomous surveillance, firefighting, and emergency medical support and proposes a staged autonomy framework incorporating perception, cognition, control, and coordination modules.
Hiroshi Tanaka· International Journal of Int...· 0 citations
: Affected by global extreme climates, ice-covering disasters occur frequently, posing a significant threat to the safe and stable operation of power transmission lines, wind turbine generators, high-speed railway contact nets, and airport infrastructure. The traditional ice removal methods generally have problems such as high risks associated with high-altitude operations, low ice-removal efficiency, and insufficient adaptability to extreme conditions. Based on this, this paper comprehensively reviews the research progress and engineering applications of AI-enabled intelligent de-icing robots, thereby providing theoretical guidance and technical support for future technology optimization and large-scale engineering applications. Focusing on the “perception–decision–execution” technical framework of intelligent de-icing robots, the key technologies, including multi-source information fusion perception, intelligent ice-condition identification, autonomous path planning and motion control, reinforcement learning-based decision-making, and composite de-icing actuators, are comprehensively analyzed. Typical engineering applications in power transmission lines, wind turbine blades, high-speed railway catenary systems, and civil aviation airports are also reviewed and evaluated. The integration of artificial intelligence technologies significantly enhances the environmental perception, autonomous decision-making, and adaptability of intelligent de-icing robots under complex operating conditions, enabling the transition from manual operation to autonomous de-icing. These technologies effectively improve operational safety, efficiency, and intelligence levels. At present, intelligent de-icing robots have been successfully applied in the power, transportation, and renewable energy sectors, demonstrating promising engineering performance. Intelligent de-icing robots have become an important technological approach for improving the anti-icing and de-icing capabilities of critical infrastructures and exhibit broad application prospects. However, significant challenges remain in complex environments, adaptability to extreme operating conditions, energy supply and endurance, and the establishment of technical
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.
Pooja Agarwal, Rakesh Chandra· International Journal of Int...· 0 citations
Flexible manufacturing, characterized by high-mix, low-volume, and highly variable production, demands robotic systems with strong adaptability, dexterity, and intelligence that conventional offline-programmed industrial robots cannot provide. This paper presents a systematic review of key technologies for robot embodied intelligence oriented toward flexible manufacturing, organized around the closed loop of perception, decision-making, and execution. The purpose is to clarify the current research landscape, identify core technical bottlenecks, and outline future directions for embodied-intelligent manufacturing. Adopting a literature-analysis and comparative-review method, the study examines representative advances at three levels: multimodal environmental perception and real-time modeling, flexible adaptive precision manipulation, and intelligent decision-making for process planning and scheduling. The review finds that multimodal fusion and semantic SLAM are overcoming perception bottlenecks, that deep learning and force/position hybrid control are balancing flexible adaptability with high-precision operation, and that deep reinforcement learning and large models are advancing intelligent process planning. It concludes that data scarcity, model reliability, software-hardware integration, and ethical-legal standards remain the principal challenges to large-scale industrial deployment.
Zheng-Yang Chen· Advances in Engineering Inno...· 0 citations
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