Days 01–03 — Foundation: Hardware, Control & Peripherals
Hardware & System Setup
Mount and provision the NVIDIA Jetson edge computer, bringing the robot's control layer online via its API and communication bridge.
Sensors & Development Environment
Integrate the depth camera and LiDAR with ROS 2 drivers. Includes environment validation, GitHub setup, configuration practice, and troubleshooting.
Days 04–05 — Mapping & Autonomous Navigation
Mapping with LiDAR-Inertial SLAM
Build accurate 3D maps using tightly fused LiDAR-inertial SLAM.
Autonomous Navigation
Convert maps into navigation-ready occupancy grids. Tune the navigation stack and watch the robot plan and execute paths autonomously through the mapped environment.
Days 06–07 — Computer Vision: Recognition & Detection
Edge-Based Computer Vision
Deploy accelerated vision pipelines on the edge.
Recognition & Tracking
Implement facial recognition with identity enrollment and allow/deny lists, along with real-time object detection and tracking using YOLO-family models. Connect both capabilities to robot behaviour through the unified API.
Days 08–09 — Voice Interaction: Reflex & Cognitive Pipelines
Voice Interaction Pipeline
Assemble the complete microphone-to-action audio chain, including wake-word detection, speech-to-text, and speech synthesis.
LLM-Powered Robot Commands
Add an edge-deployed LLM that converts free-form language into multi-step robot commands.
Day 10 — Full System Unification & Showcase
System Integration
Combine vision, voice, navigation, and control on a single edge computer.
Final Demonstration
Run an end-to-end demonstration scenario and present a working system with a documented GitHub portfolio.
