Integrating AI-Driven Drone Navigation to Enhance Blasting Assessment, Haul-Road Monitoring, and Operational Safety
Year: 2025
Type: article
Abstract: Mining operations increasingly depend on rapid, high-fidelity data acquisition to support decisions related to blasting performance, haul-road condition monitoring, and overall site safety.Traditional survey and inspection approaches typically conducted manually or with conventional remotely piloted drones often struggle to deliver the precision, coverage, and real-time responsiveness required in modern, high-throughput mining environments.Recent advancements in artificial intelligence (AI) have introduced new capabilities in autonomous drone navigation, including adaptive flight control, obstacle avoidance, multi-sensor fusion, and intelligent path planning.These innovations enable drones to operate reliably in complex, GPS-denied zones, handle highly variable topography, and maintain stable performance under conditions of dust, vibration, and poor visibility.This article examines how AI-enabled navigation transforms three critical operational domains: blasting assessment, haul-road monitoring, and safety management.For blasting, autonomous drones leverage real-time LiDAR-SLAM mapping, photogrammetry, and AI-based fragmentation analysis to evaluate blast outcomes more quickly and accurately than manual inspections.For haul-roads, AI-driven drones capture surface anomalies, ruts, and gradient inconsistencies, enabling predictive maintenance and reducing fuel consumption and tire wear.In safety applications, intelligent drones provide continuous monitoring of hazardous areas, detect wall deformation, and support emergency response with rapid situational imaging.By synthesizing navigation algorithms, sensing technologies, and operational workflows, the study demonstrates that AI-driven autonomous drone systems significantly improve data reliability, reduce inspection-related downtime, and strengthen risk-mitigation efforts across mining operations.The article concludes by outlining implementation considerations, including sensor payload selection, regulatory compliance, enterprise integration, and training requirements for minesite personnel.
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4416580110
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