AI driven heavy construction machinery

Desired outcome

Our project aims to revolutionize heavy machinery management by developing an integrated digital ecosystem that leverages a centralized digital twin platform combined with advanced AI-driven remote operations. The core idea is to retrofit existing equipment with AI capabilities and create a virtualized operational environment.

Initial Problem Description

To train the AI model, we need to gather as much data as possible from different sensors. And to train we need the sensory data such as oil pressure, GPS, Gyroscope (IMU),​ Temperature Sensor,​ Sound Sensor.
These sensors have been placed on different locations of the virtual sensor and we need to gather and create data to train our excavator.

Context

In this initial phase, we perform a thorough system and operational analysis of the excavator, establishing performance benchmarks and training requirements. We will define precise objectives and gather the necessary datasets, ensuring that the subsequent AI training is both targeted and effective.

Connection to cross-cutting areas

The construction and mining industries are facing mounting challenges, including escalating operational costs, inefficiencies, and persistent safety issues. By uniting state‐of‐the‐art retrofitting technologies with a digital twin platform, our approach surpasses conventional methods and current state‐of‐the‐art practices, delivering unprecedented control and efficiency.

Input

The "scenarios" are data gathering and AI training. We will help the students to create scenarios and organize them into a structured, stage-based plan designed to meet our project objectives. The scenarios will then be used in a AI training program, where we will develop and fine-tune our algorithms.

Expectations

Initial data (scenarios) collection and AI training will evolve into real time data collection and field testing ensuring that each stage directly contributes to achieving our project objectives offering a automated heavy construction machinery.

Desired Team Profile

We seek a team eager to learn data generation and simulation, develop environments in Gazebo, explore ROS2, and contribute to AI system training and development. A strong willingness to learn and innovate is more important than specific academic backgrounds.

Additional Information

Before submitting their information, teams should understand the current state of AI-driven automation in construction and mining, including key industry players, adoption challenges, and integration difficulties. A major challenge is the availability of real-world data, making simulation environments critical for training AI systems. Teams should be eager to explore ROS2, Gazebo, and AI development, with a strong emphasis on innovation, adaptability, and practical problem-solving.

Related Keywords

• Industrial Products
• Industrial Automation
• Industrial measurement and sensing equipment
• Process control equipment and systems
• Robotics
• Machine vision software and systems