Autonomous Mining - Leveraging Silicon Photonics-Integrated LiDAR for Driverless Systems (Internship) King Abdullah University of Science and Technology (KAUST)

Overview

This proposal addresses the urgent need for smart construction solutions in the Kingdom of Saudi Arabia (KSA), given the challenging desert terrain and extreme weather conditions. Autonomous haulage trucks, equipped with advanced sensors such as light detection and ranging (LiDAR), present a promising solution for efficient, large-scale material transport in these harsh environments. 

However, deploying this technology presents challenges, primarily because current LiDAR systems are not tailored for desert environments. Market-available LiDAR products, generally designed for urban traffic scenarios, fall short of the stringent requirements in autonomous mining applications.

Key Facts

Given the coexistence of autonomous and manned vehicles in mining environments that creates complex traffic scenarios, a prediction algorithm will be developed based on inverse reinforcement learning, to forecast decisions and trajectories of vehicles identified by LiDAR. Altogether, this comprehensive strategy will enhance navigational safety and operational efficiency of autonomous haul trucks within the challenging desert landscapes of Saudi Arabia. Learn more with this  Autonomous Mining - Leveraging Silicon Photonics-Integrated LiDAR for Driverless Systems (Internship) project from King Abdullah University of Science and Technology (KAUST) .

Recommended Student Background

• Semiconductor physics

• Integrated photonics

• Semiconductor lasers