2018· International Journal of Intelligent Automation & Robotics Engineering· 0 citations
TL;DR
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.
Abstract
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.
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
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
Autonomous robotic systems are transforming industries such as automation, healthcare, transportation, defense, and intelligent services. A major challenge in robotics is motion planning in dynamic and uncertain environments, where robots must navigate safely and efficiently. Traditional algorithms like Dijkstra’s, A*, Probabilistic Road Maps, and Rapidly Exploring Random Trees perform well in static environments but struggle with moving obstacles, sensor uncertainty, and real-time decision-making. Artificial Intelligence (AI) has improved robotic motion planning through adaptive learning, predictive decision-making, reinforcement learning, fuzzy logic, neural networks, and evolutionary optimization. These techniques enable robots to learn from their environment and optimize navigation over time. This paper reviews AI-based adaptive motion planning methods developed before 2019 and examines their role in path optimization, obstacle avoidance, localization, and decision-making. It also proposes a hybrid framework combining sensor fusion, environment mapping, fuzzy inference systems, and reinforcement learning for real-time path optimization. Simulation results demonstrate that AI-based adaptive planning achieves better navigation accuracy, obstacle avoidance, computational efficiency, and environmental adaptability compared to traditional methods.
Michael Anderson· International Journal of Int...· 0 citations
The findings highlight the need for harmonized governance, interoperable digital infrastructure, multidisciplinary collaboration, and implementation-focused research to facilitate the safe and effective integration of autonomous technologies into future disaster management systems, with important implications for policymakers, emergency management agencies, humanitarian organizations, and researchers.
R. Ohaka, A. D. Akinyemi, Ugochukwu Udonna Okonkwo· Journal of Computers and App...· 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
Autonomous and intelligent transportation systems operate in complex urban environments where safety depends on interactions among vehicle behavior, environmental conditions, and vulnerable road users (VRUs) such as pedestrians and cyclists. Most advanced driver assistance systems (ADAS) employ reactive mechanisms that activate only after hazards have emerged, a critical limitation underscored by rising VRU fatalities in the United States. This study introduces PRISM (Proactive Risk Intelligence and Safety Management), an agentic multi-model safety architecture that transitions from reactive crash avoidance to proactive, continuous risk management. PRISM employs inverse crash-probability modeling to convert binary crash classifiers into dynamic, interpretable safety scores. Three specialized models addressing trajectory kinematics, environmental risk, and VRU interaction operate concurrently, coordinated by a reasoning layer incorporating reinforcement learning, contextual memory, and feature-level attribution. The system provides graduated safety interventions across four tiers, from silent monitoring to emergency alerts. Unlike rule-based systems with static thresholds, PRISM dynamically adjusts safety parameters in real time. Validated across 1,296 scenarios from three naturalistic driving datasets without dataset-specific retraining, the system yielded a mean safety score of 68 out of 100, classified 77.6% of scenarios as advisory, and flagged a near-miss rate of 3.8%, with 11% of scenarios escalating to intervention or emergency response. Feature attribution consistently identified trajectory risk and VRU proximity as primary safety factors. PRISM provides a unified, interpretable framework for proactive transportation safety with emphasis on VRU risk reduction in dense urban environments.
