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Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 04:44:15am America, Santiago

Daily Overview

Session

Student Paper (SP) 02
Location: Room 07: Antartica

Presentation 3

2:20pm - 2:30pm

A Multimodal 3D Perception-Based Autonomous Robotic Architecture for Underground Mining: Predictive Planning and Dynamic Risk-Aware Control

Jose Luis Segundo Manayay¹, Javier Yanpier Garay Yovera², Raúl Gianmarco Chávez Chávez³, Francisco Rodríguez Huiman⁴, Fidel Angel Castro Suazo⁵, Roberts Neptali Antayhua Alvarez⁶, Jhojan Antony Espinoza Coronel⁷

1: Universidad Nacional de Ingeniería, Perú; 2: Universidad Nacional de Ingeniería, Perú; 3: Universidad Nacional de Ingeniería, Perú; 4: Universidad Nacional de Ingeniería, Perú; 5: Universidad Nacional de Ingeniería, Perú; 6: Universidad Nacional de Ingeniería, Perú; 7: Universidad Nacional de Ingeniería, Perú

Risk inspection and mitigation in underground mining pose critical challenges due to unstructured environments, limited visibility, and geomechanical instabilities. This paper presents an autonomous quadruped robotic architecture based on multimodal 3D perception fusion for safe navigation and early hazard detection in complex mining scenarios. The system integrates LiDAR, computer vision, and inertial data through probabilistic fusion to generate consistent three-dimensional maps using real-time SLAM.
A risk-aware predictive planning framework is formulated as an optimization problem with dynamic weighting of critical zones and safe trajectory generation under kinematic and dynamic constraints. A robust dynamic controller ensures stability and adaptability over irregular terrain and external disturbances.
The architecture is validated through high-fidelity ROS~2 simulations and laboratory experiments emulating underground gallery conditions. Results confirm real-time computational feasibility and demonstrate improved obstacle avoidance robustness and superior chassis stabilization compared to reactive navigation strategies. The proposed integration of multimodal perception, predictive planning, and risk-oriented dynamic control establishes a scalable framework for safe automation in underground mining.