Systems, Vol. 13, Pages 799: Semantic Priority Navigation for Energy-Aware Mining Robots

Systems doi: 10.3390/systems13090799

Authors:
Claudio Urrea
Kevin Valencia-Aragón
John Kern

Autonomous navigation in subterranean mines is hindered by deformable terrain, dust-laden visibility, and densely packed, safety-critical machinery. We propose a systems-oriented navigation framework that embeds semantic priorities into reactive planning for energy-aware autonomy in a Robot Operating System (ROS). A lightweight Convolutional Neural Network (CNN) detector fuses RGB-D and LiDAR data to classify obstacles like humans, haul trucks, and debris, writing risk-weighted virtual LaserScans to the local planner so obstacles are evaluated by relevance rather than geometry. By integrating class-specific inflation layers in costmaps within a cyber–physical systems architecture, the system ensures ISO-compliant separation without sacrificing throughput. In Gazebo experiments with three obstacle classes and 60 runs, high-risk clearance increased by 34%, collisions dropped to zero, mission time remained statistically unchanged, and estimated kinematic effort increased by 6% relative to a geometry-only baseline. These results demonstrate effective systems integration and a favorable safety–efficiency trade-off in industrial cyber–physical environments, providing a reproducible reference for scalable deployment in real-world unstructured mining environments.

Source link

Claudio Urrea www.mdpi.com