Professional Certification

The Construct Robotics Masterclass

Advanced ROS Development Program

Comprehensive robotics training covering autonomous navigation, computer vision, motion planning, and manipulation through hands-on checkpoint projects and a final capstone demonstration.

Program Overview

The Construct Robotics Masterclass is an intensive, project-based program focused on professional-level ROS (Robot Operating System) development. The curriculum progresses through three checkpoint projects demonstrating fundamental robotics concepts, culminating in a comprehensive capstone project that integrates multiple advanced systems.

Each project builds upon previous concepts, progressing from basic navigation and control to sophisticated perception-based manipulation systems.

1

Checkpoint 1: ROSbot XL - PID Navigation

Autonomous maze navigation using PID control

Project Summary

Developed an autonomous navigation system for the ROSbot XL holonomic robot, implementing PID (Proportional-Integral-Derivative) control for precise movement through complex maze environments. The system integrates vision-based obstacle detection with feedback control for smooth navigation.

Key Achievements

  • Implemented and tuned PID control algorithms for velocity and heading
  • Integrated camera-based obstacle detection for real-time perception
  • Programmed dynamic speed and direction adjustments
  • Successfully navigated complex maze scenarios autonomously

Technologies

ROS Python PID Control Computer Vision OpenCV Gazebo
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2

Checkpoint 2: UR3e Vision-Guided Manipulation

From hardcoded movements to 3D perception-based control

Project Summary

Developed sophisticated manipulation capabilities for the UR3e robotic arm through a two-phase progression. Started with basic hardcoded joint control and advanced to computer vision-based perception using point clouds and MoveIt! for intelligent object manipulation.

Key Achievements

  • Programmed forward and inverse kinematics for precise arm control
  • Implemented point cloud processing for 3D object perception
  • Integrated MoveIt! framework for advanced motion planning
  • Developed vision-guided pick-and-place operations

Technologies

ROS MoveIt! Python C++ Point Cloud Library 3D Vision Inverse Kinematics
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3

Checkpoint 3: Autonomous Warehouse Robot

Three-phase progression to full autonomy using SLAM

Project Summary

Progressively developed autonomous capabilities for the RB-1 warehouse robot through three distinct phases: hardcoded navigation, SLAM-based mapping and localization, and finally complete autonomous operation with dynamic path planning and obstacle avoidance.

Key Achievements

  • Implemented SLAM (Gmapping) for real-time environment mapping
  • Configured ROS Navigation Stack for autonomous path planning
  • Developed obstacle detection and dynamic re-planning capabilities
  • Achieved fully autonomous shelf pickup and delivery operations

Technologies

ROS SLAM (Gmapping) Navigation Stack Python C++ Path Planning Sensor Fusion
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4

Checkpoint 4: TurtleBot3 Physical Robot Assembly

Hardware assembly, ROS1/ROS2 integration, and autonomous mapping

Project Summary

Completed the full assembly and programming of a physical TurtleBot3 robot using Raspberry Pi. Successfully integrated both ROS1 and ROS2 navigation stacks, implementing SLAM localization to enable the robot to autonomously explore, map its environment, and navigate without predefined paths.

Key Achievements

  • Physically assembled TurtleBot3 hardware from components
  • Configured Raspberry Pi for robotic control systems
  • Integrated both ROS1 and ROS2 navigation frameworks
  • Implemented SLAM (Gmapping/Cartographer) for autonomous mapping
  • Achieved autonomous exploration and navigation capabilities

Technologies

TurtleBot3 Hardware Assembly Raspberry Pi ROS1 ROS2 SLAM (Gmapping) Cartographer Autonomous Navigation
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Capstone Project: Vision-Guided Coffee Delivery

End-to-end AI-powered robotic manipulation system

Project Summary

The final capstone project integrated all skills from the masterclass into a comprehensive system: a fully autonomous coffee delivery robot using YOLOv8 computer vision for target detection, MoveIt! for motion planning, and precise robotic manipulation to pick and place coffee cups without spilling.

Key Achievements

  • Trained custom YOLOv8 model for real-time target detection
  • Implemented complete ROS architecture orchestrating vision, planning, and control
  • Developed collision-free Cartesian path planning with MoveIt!
  • Achieved 100% autonomous operation from perception to execution
  • Successfully demonstrated in live presentation (YouTube: 1:33:33 runtime)

Technologies

ROS YOLOv8 MoveIt! PyTorch Python C++ Computer Vision Motion Planning

Program Impact & Skills Acquired

Core Competencies

  • Advanced ROS/ROS2 architecture and development
  • Motion planning and trajectory optimization
  • Computer vision and perception systems
  • SLAM and autonomous navigation
  • Robotic manipulation and control theory

Professional Development

  • System integration and debugging
  • Project planning and execution
  • Technical documentation and presentation
  • Real-world robotics deployment strategies
  • Industry-standard best practices
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