Advanced Robotics Hardware Curriculum

How intelligent systems interact with the real world

The Hardware Curriculum at ARCSA focuses on robotics as a discipline of embodiment: how software, sensors, and intelligence converge to create machines that perceive, decide, and act in the physical world.

It forms one half of the Artificial Intelligence and Robotics (AIR) program and alternates with the software curriculum across terms. While the software curriculum explores intelligence in abstraction, the hardware curriculum explores how that intelligence is realised in real systems.

What Hardware Means in Artificial Intelligence and Robotics

In our Artificial Intelligence and Robotics Program (AIR), hardware does not refer to mechanical assembly or electronic kits. All advanced robotics work is built on ROS 2, the industry-standard robotics framework taught as part of AIR.

It means:

Autonomous robots with sensors and perception
AI-enabled decision-making in real environments
Robotics systems that combine software, hardware, and data
Understanding how intelligent machines behave outside simulations

Students do not build robots from scratch.

They program, analyse, and control advanced robotic systems, just as they would in a university or research environment.

A Systems Approach to Robotics

Modern robotics is not about individual components.

It is about systems.

The hardware curriculum is designed around this idea. Students learn how sensing, perception, planning, and action must work together reliably in dynamic environments.

They are encouraged to think in terms of:

Inputs and outputs
Feedback loops
Constraints and trade-offs
Reliability and safety
Real-world unpredictability

This systems thinking is one of the most essential skills students carry forward into tertiary studies.

Autonomous Robots and Perception

A central theme of the hardware curriculum is robot autonomy.

Students work in the BotBox Robotics Lab with AI-enabled robots equipped with modern sensors such as:

LIDAR, used for mapping, localisation, and obstacle detection
Cameras, used for visual perception and interpretation
Gripper mechanisms, used for interaction and manipulation

Rather than issuing direct commands, students program robots to perceive their environment, make decisions, and act autonomously.

This shift—from control to autonomy—is fundamental to understanding modern robotics.

Drones and AI in Motion

Another important component of the hardware curriculum is AI-enabled drone programming.

Drones provide a powerful learning platform because they operate in three-dimensional space and require constant perception, decision-making, and control.

Within this context, students explore how AI can be used for:

Autonomous navigation
Obstacle avoidance
Pattern recognition and localisation
Learning through feedback and reinforcement

The emphasis is not on flying for its own sake, but on understanding how intelligent systems operate under real-world constraints.

Smart City Systems and Applied Robotics

The hardware curriculum also extends beyond individual robots into larger intelligent systems, often described under the umbrella of smart cities.

Students study how robotics, sensors, and AI can work together to manage complex environments, such as:

Traffic systems
Parking and vehicle movement
Monitoring and anomaly detection
Infrastructure-aware automation

These examples help students see robotics not as isolated machines, but as part of larger socio-technical systems.

Robotics as Applied AI

Throughout the hardware curriculum, robotics is treated as Applied Artificial Intelligence.

Students encounter questions such as:

How reliable must perception be before a robot can act?
What happens when sensor data is noisy or incomplete?
How should a system behave when it is uncertain?
How do we test and validate behaviour in the real world?

These questions do not have simple answers, and that is precisely the point. Students learn to reason, test, and iterate.

How This Prepares Students for the Future

The hardware curriculum prepares students for:

University studies in robotics, mechatronics, engineering, and AI
Understanding autonomous systems beyond theory
Working with complex, sensor-rich machines
Thinking critically about safety, reliability, and responsibility

It also gives students a deeper appreciation of how difficult it is to make machines behave intelligently in the real world.

In Context with the Artificial Intelligence and Robotics (AIR) program

Within the Artificial Intelligence and Robotics (AIR) program, the hardware curriculum alternates with the software curriculum.

Students repeatedly move between:

Designing intelligence
Embedding intelligence
Observing intelligence in action

Each cycle strengthens both technical understanding and intellectual maturity.