Level 2: Robotics and AI Acceleration

The course consists of four interconnected pillars, ensuring a balance between theoretical knowledge, practical implementation, and business development.

Step-by-Step Learning Roadmap

Each pillar follows a hands-on, project-based approach, ensuring technical and business skills evolve in parallel.

Pillar 1: Mastering Robotics and AI Fundamentals

Goal: Build a strong foundation in robotics, AI, and automation using OpenAMR as the core platform, Deep Tech & Academics.

Chassis and Structural Design

• Materials: Composites, metals, and lightweight polymers
• 3D modeling with Fusion 360 and SolidWorks
• Structural analysis (FEA, weight optimization)
• Additive & subtractive manufacturing (3D printing, CNC machining)

Electronics and Embedded Systems

• PCB design, EMI and power distribution for AMRs
• Communication protocols (MODBUS, CAN, SPI, UART, etc.)
• Microcontrollers & FPGA (Arduino, STM32, Xilinx)
• Sensor integration
• Battery management, charging systems, motor drivers

Automation & Control Systems

• Embedded programming (C, C++, MicroPython)
• Microcontroller programming (STM32, ESP32)
• PID, MPC, and other control strategies

Localization & Navigation Software Development for Robotics

• ROS2-based navigation and sensor fusion
• SLAM, AMCL, path planning (A*), Kalman filter, etc.
• Localization & navigation (SLAM, GPS-RTK)
• Obstacle avoidance & path planning (A*)
• Fleet management systems

Hands-on Projects

• Design and 3D-print an AMR chassis prototype
• Build a sensor-integrated embedded system (IMUs, LiDAR, ultrasonic)
• Implement A* path planning for AMR navigation

Pillar 2: AI-Driven Robotics and Industrial Automation

Goal: Apply AI to perception, decision-making, and optimization in robotic systems.

Robotic Arm Integration

• Industrial robotic arms (KUKA, UR, Fanuc)
• ROS-based motion planning
• End-effector development (grippers, tools)

Computer Vision and Machine Learning

• AI-powered object recognition (YOLO, OpenCV)
• Image segmentation, feature extraction
• 3D vision & LiDAR processing
• Deep learning for robotics (TensorFlow, PyTorch)
• Sensor fusion & multispectral imaging

Reinforcement Learning and LLM-Based Interfaces

• AI decision-making for autonomous robot control
• Training reinforcement learning models in Google Colab
• LLM-based natural language interfaces for robot control and user interaction

AI in Logistics and Warehouse Automation

• AI-powered sorting, picking, and order fulfillment
• Real-time tracking and predictive demand planning
• AI-based cloud solutions for fleet management and logistics optimization

Hands-on Projects

• Develop an AI-powered perception system (object detection using OpenCV and TensorFlow)
• Implement robotic grasping for warehouse automation
• Simulate AI-driven warehouse logistics (Gazebo and ROS2)
• Develop an LLM-based user interface for natural language robotic interaction

Pillar 3: Building a Scalable Robotics Business

Goal: Transition from prototype to real-world deployment, integrating robotics into industry applications.

Prototyping and Manufacturing

• CNC machining, 3D printing, rapid prototyping
• PCB production, electronic assembly, testing

Product Development & Manufacturing

• Design for manufacturability (DFM)
• Supply chain management
• CE/FCC certification & compliance

Fleet Management and Automation

• Multi-robot coordination (OpenRMF, Fleetbase)
• AI-powered warehouse layout optimization
• Cloud-based robotics solutions for remote operation and monitoring

Industry-Specific Applications

• E-commerce and last-mile delivery
• Industrial automation (cobots, pick and place)

Hands-on Projects

• Build a fully functional AMR with AI-powered navigation
• Deploy an AI-driven pick-and-place cobot
• Implement warehouse robotics in a simulated environment
• Develop a cloud-based AI fleet management solution

Pillar 4: Robotics Entrepreneurship and Commercialization

Goal: Transform a robotics solution into a scalable business (Robotics-as-a-Service model).

Market Research and Financial Modeling

• Competitor analysis, pricing strategies, revenue forecasting
• Investment strategies for robotics startups

Sales and Growth Strategies for Robotics Businesses

• B2B vs. B2C business models (Automation-as-a-Service)
• Growth hacking for robotics startups

Scaling Robotics Solutions into Commercial Ventures

• Transitioning from prototype to industry-ready solutions
• Scaling production and logistics with ERP/CRM tools
• Lean startup methodology and sustainability

Hands-on Projects

• Develop a go-to-market strategy for a robotics product
• Simulate a robotics startup financial model
• Pitch a robotics business concept to industry experts

Additional Hands-on Projects and DIY Kits

These advanced projects provide additional hands-on experience and can be offered as purchasable DIY kits for independent learning:

Defense Radar and Object Tracking

• Phased Array Antenna (PAA) with 33mm wave microstrip modules
• AI-based object tracking and real-time radar data processing

Agriculture Weed Removal Robot

• mmWave PAA-based system for targeted weed elimination
• Steering microwave beams for non-chemical weed removal

Disinfection and Sanitizing Robot

• UV lamp and ultrasonic nebulizer for surface and air sanitization
• H2O2 fogging with UV decomposition to OH radicals

GPR-Based Demining System

• Phased Array Ground Penetrating Radar (GPR) for landmine detection
• AI-driven anomaly detection for subsurface mapping

➡ Check on the previous level: Level 1

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