🚀 Robotics, Vision & Control: The 5 Ultimate Technical Challenges

This repository contains my C++ / ROS 2 / OpenCV implementations of five advanced robotics challenges.

More than just basic tutorials, these exercises are designed to tackle common pitfalls and edge cases encountered in professional robotics software engineering. From low-level memory management and asynchronous architecture to sensor fusion and algorithmic complexity, this workspace proves my ability to write production-ready code.

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🛠️ Build & Run Instructions

# Create a workspace
mkdir -p ~/ros2_challenges_ws/src
cd ~/ros2_challenges_ws/src

# Clone the repository
git clone [https://github.com/NicoC54/Robotics-Vision-Control-Challenges.git](https://github.com/NicoC54/Robotics-Vision-Control-Challenges.git)

# Build the workspace
cd ..
colcon build --symlink-install
source install/setup.bash

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🧠 The Challenges

✅ Level 1: The Perception Pipeline (Pure C++, Threading & Polymorphism)

Objective: Design the software core of a camera node. It continuously captures images in one thread while applying a "Plug & Play" sequence of filters (e.g., Gaussian Blur, Canny) in another.

• Engineering Focus: Multithreading, Mutexes, and memory safety.
• Source Code: View perception_pipeline/src

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✅ Level 2: The A* Path Planner (C++ STL, Big O & ROS 2 Services)

Objective: A complete ROS 2 Service/Client architecture. The node exposes a GetPath Service taking a 2D occupancy grid, a start point, and a goal, computing the shortest path using the A* algorithm. The Client node sends the request and processes the trajectory.

• Engineering Focus: Algorithmic optimization and strict C++ Standard Template Library (STL) container selection.
• Source Code: View astar_service/src

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✅ Level 3: The Pursuit Controller (ROS 2 Actions, PID & TF2)

Objective: A ROS 2 Action Server for a mobile robot. Given a "Follow Object X" goal, the server continuously listens to the TF2 tree to compute the distance to the target, applies a PID controller to output cmd_vel, and streams the remaining distance as feedback.

• Engineering Focus: Asynchronous control loops and automatic control theory.
• Source Code: View action_pid/src

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✅ Level 4: The 3D Pose Estimator (OpenCV, TF2 Broadcaster, Quaternions)

Objective: Extract 3D spatial data from a 2D image. The node detects an ArUco marker, uses the camera's intrinsic matrix to compute its 3D pose via solvePnP, and broadcasts it to the ROS 2 network.

• Engineering Focus: Spatial vision and 3D mathematics.
• Source Code: View aruco_node/src

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✅ Level 5 (Boss): Zero-Copy State Estimator (Kalman Filter, QoS, Intra-process)

Objective: Two ROS 2 Component Nodes (a Publisher and a Subscriber) running in the same process. Node A publishes camera frames. Node B subscribes, reads the image in Zero-Copy mode, extracts a noisy 2D position, and filters it through a Linear Kalman Filter to predict the true state.

• Engineering Focus: IPC (Inter-Process Communication), ROS 2 Node Composition, and Sensor Fusion.
• Source Code: View tracker_kalman/src

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📫 Let's Connect

I am currently seeking full-time opportunities as a Robotics Software Engineer or Computer Vision Engineer.