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Autonomous Warehouse Task Simulation & CV-Based Docking

This applied robotics lesson focuses on simulating an AI-powered mobile robot performing logistics tasks in a warehouse environment. Students implement computer vision-based docking, waypoint navigation, and task execution using the ROS 2 framework and modern perception tools.

By combining vision, navigation, and behavioral planning, this lesson mirrors the systems used in real-world automated warehouses operated by companies like Amazon, DHL, and various robotics startups.

Autonomous Warehouse Mapping Overview

Technologies Covered

  • AprilTag or ArUco marker detection using OpenCV
  • Pose estimation and alignment logic for precision docking
  • Task-based navigation using waypoints and behavior trees (BTs)
  • Integrating computer vision feedback into ROS 2 control loops

System Overview

  1. Visual tags (AprilTag or ArUco) are placed on docking stations or loading areas.
  2. A camera onboard the robot detects the tags and estimates their relative pose (position and orientation).
  3. The robot uses this pose information to align and dock accurately for task execution (e.g., loading/unloading).
  4. Navigation goals are organized using waypoints or behavior trees for multi-step tasks.
  5. ROS 2 nodes process vision input and issue movement commands via the Nav2 stack or direct velocity control.

Learning Outcomes

  • Detect and localize fiducial markers using OpenCV and ROS-compatible libraries
  • Calculate pose transforms and apply them for autonomous docking
  • Develop structured task flows using waypoints and behavior trees
  • Integrate computer vision feedback into the robot’s navigation logic
  • Simulate real warehouse automation workflows using ROS 2 and Gazebo

Optional Extensions

  • Replace static markers with dynamic QR codes for multi-target environments
  • Use a depth camera (e.g., RealSense or ZED) for obstacle-aware docking
  • Integrate voice or GUI interfaces for assigning warehouse tasks
  • Log task completions and docking metrics to a cloud-based dashboard
  • Simulate multi-robot coordination and shared docking stations