🌐 Codestral ROS2 Gen: Network Scanner Extension Now Available!

⚡️Here it is - the second release of Codestral ROS2 Gen with a powerful new feature: the NetworkScanner!

🔍 What's New in This Release
The NetworkScanner module with efficient network discovery capabilities:
• Fast ICMP-based scanning for host discovery
• Asynchronous packet handling for optimized performance
• Configurable scan parameters (timeout, interval, etc.)
• Seamless integration with the existing ROS2 generation pipeline

🛠 How It Works
The NetworkScanner uses optimized ICMP echo requests (pings) to detect active hosts on a network. It employs an innovative approach with:
• Synchronous packet sending for precise timing control
• Asynchronous response collection for efficient handling
• Smart timeout management for reliable results
• Clean ROS2 message publishing for network status information

🧪 Key Components
• network_scanner.py: Core scanning orchestration
• network_host.py: Host state management
• scan_operation.py: Context-managed scanning operations
• network_parser.py: Network targets parsing

Full codebase documentation is available on projects's GitHub pages 📙

🚀 Try It Yourself
The detailed example documentation shows how to generate your own network scanner nodes. You can even use the standalone nscan command-line tool for quick testing!

This extension builds upon core generation system, demonstrating how the Codestral generator can create complex, functional ROS2 components with system-level interactions. 🤖

#ROS2 #AI #NetworkScanning #Robotics #CodeGenerating #Codestral

👨‍🔬Testing Results: ROS2 Network Scanner Generation

I want to share the results from my test of ROS2 Network Scanner generation example.

After running 30 iterations of generating the ROS2 Network Scanner:
• Total test duration: ~6 hours 15 minutes
• Average successful generation time: ~2 minutes per attempt
• Distribution of attempts: Right-skewed (median: 4, mean: 6.7)

This means that, on average, the generator produces working code in about 13 minutes - quite reasonable performance for automated code generation in my opinion!

Failure Analysis
Looking at where generation stopped, the distribution clearly demonstrates the generator's stability:
• Over 80% stopped at the testing stage
• ~15% were successful attempts
• Only about 5% failed during the PARSING or GENERATION stages

Test Coverage Patterns
Examining the test pass rates revealed two distinct patterns:
• Basic functionality (7 tests): Node startup with valid/invalid parameters and handling overlapping scans using nscan utility
• Advanced scenarios (9 tests): Including handling invalid JSON format in the node <-> nscan interface and managing outdated scan results

This suggests that generating code with specific behavior for edge cases remains challenging.

I've included all metrics and analysis notebooks in my project repository, so feel free to explore the data yourself!

#ROS2 #AI #NetworkScanning #Robotics #CodeGenerating #Codestral #testing

🌱 Can You Simulate Organic Life with ROS Nodes? Absolutely! ✨

I've been exploring the idea of using ROS2 nodes not just for robots, but as building blocks for simulating organic life—and the results are super promising!

Why is this approach interesting?
• Each ROS node acts like a "cell" or "organ," handling one function (movement, sensing, decision-making, etc.).
• The distributed, modular nature of ROS is perfect for mimicking how biological systems work together in real life.
• Nodes communicate via topics and services—very much like cells communicate through signals in nature.
• With ROS’s flexibility, you can easily scale up complexity, experiment with emergent behavior, and create fantastically detailed digital creatures.

What’s possible?
• Model complex, bio-inspired behaviors (think neural signals, homeostasis, swarming).
• Use ROS tools like Gazebo for 3D, physics-based environments.
• Mix and match algorithms in Python or C++ for rich, dynamic "organisms."
• Great for experimenting with concepts from biology, robotics, or artificial life.

Challenges?
Real-world biology is still way more complicated, but ROS nodes give us an amazing, practical starting point. Visualization and detailed modeling might need extra tools, but the pathway is wide open for creativity.

Bottom line: Using ROS nodes to simulate organic forms is not just possible—it’s a powerful, scalable way to blend robotics, biology, and AI. Can't wait to see where this leads!

🔧 Interested in the project or have questions? Join the discussion and let's build some digital life together!

#ROS2 #AI #BioInspired #OrganicSimulation #Robotics

⚡️They're Alive! 🐢

Simple Kinesis Turtle Simulation.
Experience virtual "life" in action as turtles move dynamically inside a simulated environment in temperature field. This project uses ROS2 and the classic turtlesim application to bring simple, engaging bio-inspired behaviors to life.

What is Kinesis?
Kinesis describes a non-directional movement response to stimuli, commonly observed in living organisms. In biology, it's how simple creatures respond randomly to environmental changes—think of a bug moving faster in open sunlight to find shelter.

🤖This is the first part of simulating organic movements, and ROS has proven to be incredibly convenient for developing such dynamic behaviors.

Want to Join or Read the Code?
Check out the project repository:👉 Project's GitHub Page

#ROS2 #Turtlesim #OrganicSimulation

🐢 Adding Taxis Behavior

The second behavior I wanted to try with turtles is called taxis motion. Unlike kinesis, where turtles just change how fast they move depending on the temperature around them, taxis means the turtle can actually sense which way the temperature gets warmer and steers itself in that direction. So, the control node finds out where the temperature goes up the most and turns the turtle to move that way, kind of like following a scent trail. A real-life example of taxis is how moths fly toward a light.

🌐 Full code on GitHub

#ROS2 #Turtlesim #OrganicSimulation

Kinesis ⚔️ Taxis - Behavior Battle

Since we have two different types of behavior, it’s very interesting to compare them on different field types to see which is better. For my tests, I set up turtles using either kinesis or taxis in the same non-uniform temperature fields. I tracked how often each turtle visited the hottest zones—places where the temperature was above 80% of the maximum.

The results aren’t so straightforward: while taxis is a bit more efficient than kinesis in simple bimodal fields, there’s no clear winner as the temperature pattern becomes more complex.

Also, since both models are quite basic, their performance might change in more realistic scenarios where agents can use smarter or more adaptive strategies.

📑 See pdf below for the full test description.

#ROS2 #Turtlesim #OrganicSimulation