Designed for future innovators, the Advanced Scientists course explores advanced robotics, automation, and AI-driven applications. Students learn how to integrate artificial intelligence and data-driven decision-making into robotic systems. They work on real-world inspired challenges, preparing them for higher-level STEM competitions and future careers in technology and engineering.

Duration : 5 Months

What Your Kids Will Learn :

• 40 in 1 Electronics Robotics Master Kit
• Basics of Arduino
• 10 Application Projects

Key Highlights:

• Introduction to AI and machine learning concepts

• Build autonomous robots with advanced sensors

• Learn real-world automation logic

Work on research-inspired innovation projects

Course goal

Introduce advanced topics: sensor fusion, autonomy, control logic, basic computer vision/machine learning classroom-friendly approaches, and system integration — preparing students for competitions and advanced study.

Representative projects

• Autonomous maze solver, vision-assisted line follower, micro-TinyML classifier, basic SLAM demo concept (using encoders + sensors).

Learning outcomes

• Understand fundamentals of autonomy and perception.

• Use pre-trained ML models or TinyML for simple inference.

• Integrate sensors, control loops and high-level decision logic.

• Document system architecture and test results.

Materials / Kit

• Advanced kit: microcontroller (Raspberry Pi / OpenMV / ESP32 with camera) or combo, motor drivers, encoders, IMU, ultrasonic/Time-of-Flight sensors, camera (OpenMV/RPI camera), power bank, breadboards, wiring, mounting hardware.

• Software: Python basics, MakeCode or MicroPython, TensorFlow Lite (if used), OpenMV IDE (if used).

Class logistics

• Class size: 6–10 students (higher instructor ratio).

• Session length: 2 hours minimum.

• Pre-req: basic programming (Python/Arduino) and mechanical understanding recommended.

Assessment & certification

• Assessment by rubric: system design, functionality, robustness, report.

• Certificate: “Advanced Scientist — Autonomous Systems & AI Basics.”

• Optional: badge for ML/Computer Vision module.

Instructor notes

• • Emphasize simulation and testing before build (saves hardware failure).

  • Use cloud/offline pre-trained models to avoid long training time.

  • Prepare spare hardware and debug checklist.