Jul 2026· Italian National Conference on Sensors· Vol 26, pp. 4258· 0 citations· 100 references
Medicine
Abstract
Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, AHS predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making within individual systems. These processes enable collision avoidance and path tracking. However, they are limited in their ability to consider the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems of AHS in surface mining. It critically analyzes the transition from autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. It assesses sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is focused on the limitations of egocentric perception models in complex surface mining ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). Perception is enhanced through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by outlining a research agenda to transition from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital-twin-integrated predictive safety systems.
Autonomous field robots are increasingly used in precision agriculture for monitoring, phenotyping, spraying, and site-specific intervention, yet reliable autonomy remains difficult under vegetation occlusion, uneven terrain, variable illumination, and intermittent communications. This review provides a deployment-oriented synthesis of navigation and sensor-fusion methods for agricultural robots, with emphasis on what is practical under field conditions rather than only in laboratory settings. The literature was examined through a structured narrative-review workflow using Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and related citation tracking, with primary emphasis on studies published between 2015 and 2025. We compare global, local, and hybrid planning methods; motion-control strategies such as PID, Pure Pursuit, and MPC; and localization pipelines that combine GNSS, IMU, LiDAR, cameras, odometry, and SLAM or Kalman-family fusion. Beyond algorithm summaries, the review links method selection to agricultural deployment constraints, including GNSS degradation, dynamic obstacles, compute limits, ROS 2 integration, time synchronization, and coordinate-frame management. The synthesis shows that no single stack is optimal across all crop systems: lightweight GNSS/IMU-based solutions remain attractive in structured open fields, whereas orchards, vineyards, and other occluded environments benefit more from tighter multi-sensor fusion and SLAM-supported localization. Finally, the review distills design guidance for sensing, planning, validation, and digital-twin-supported testing, and identifies research gaps related to robustness, benchmarking, safety, and scalable on-farm deployment.
N. Boros, Bálint Ambrus, A. Nyéki· Italian National Conference...· 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
This review provides a comprehensive synthesis of traditional and artificial intelligence (AI)-based techniques across the complete autonomous UAV navigation pipeline, including environmental perception, localization and mapping, path planning and obstacle avoidance, and motion control, together with commonly used datasets, simulation platforms, and evaluation practices.
T. Mahmood, Ali Ahmed Mirza· Scientific Journal of Engine...· 0 citations
A conceptual framework is proposed that interprets sensor fusion as a reconstructive process, transforming diverse sensory inputs into a coherent environmental model, and connects fusion strategies to key autonomous driving tasks, including object detection, tracking, localisation, and planning.
Autonomous navigation of robots is a key technology for intelligent transport, industrial automation, and service robotics. This paper reviews its core technical framework and typical applications, focusing on five closely connected modules: perception, localisation and mapping, path planning, obstacle avoidance, and decision-making control. It first explains how vision sensors, LiDAR, millimetre-wave radar, IMU, and other sensing devices support environmental perception across different scenarios, and why multi-sensor fusion is necessary to improve robustness. Then, it discusses major localisation and mapping methods, including SLAM-based approaches, as well as path planning and control algorithms such as A*, DWA, TEB, MPC, and reinforcement learning. The paper further compares the application characteristics of autonomous navigation in intelligent transportation, industrial logistics, and medical service robots, showing that different scenarios require different balances between robustness, efficiency, accuracy, safety, and energy consumption. Finally, it points out future trends, including cloud-edge-device collaboration, end-to-end learning, multimodal large models, embodied intelligence, and cooperative navigation, which may further promote intelligent and sustainable robot mobility.
Precision agriculture focuses on improving crop yield, optimizing resource use, and reducing environmental impact through data-driven decision-making. Recent advancements in UAVs, artificial intelligence, embedded systems, and remote sensing have enabled the use of autonomous drones in farming. This paper presents the design of an autonomous drone system for precision agriculture, integrating intelligent sensors, adaptive navigation, and real-time analytics. The system addresses key agricultural challenges such as climate change, soil degradation, water scarcity, and rising operational costs by replacing labor-intensive and time-consuming manual inspections with efficient aerial monitoring. The proposed modular drone system includes flight control, multispectral imaging, computer vision, IoT connectivity, and machine learning models. It emphasizes durability, energy efficiency, fault tolerance, and autonomous decision-making. Key features include sensor fusion, path planning, obstacle avoidance, and adaptive mission scheduling. Experimental results demonstrate improved monitoring accuracy, higher coverage, and reliable data collection compared to traditional methods. The study highlights the potential of autonomous drones in promoting sustainable agriculture through efficient resource management, early stress detection, and large-scale farm monitoring.
Elena Petrova· International Journal of Mod...· 0 citations
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