Which libraries and tools are frequently used in robotics for Python and C++?

[GramExplorer]

I have a basic understanding of both Python and C++, and I'm curious about the specific tools or libraries in these languages that are commonly employed in robotics. These tools don't necessarily need to be exclusive to robotics; they could be multipurpose tools that are frequently utilized in the field.

For instance, I'm aware that the Robot Operating System (ROS) is widely used, as is OpenCV for vision-related tasks, and Python's Numpy and TensorFlow for data handling and machine learning. Are there other significant tools or libraries I might be missing? For example, I've heard about MoveIt for motion planning, but I'm not sure if it's the standard choice. Is there a C++ library for linear algebra that compares to Numpy?

Additionally, are there specific styles or design patterns that are more prevalent in robotics compared to general programming in C++ or Python? What about unique programming quirks or practices in robotics that might not be covered in standard programming tutorials?

While I'm focusing on C++ and Python due to their popularity in job listings, I'm open to suggestions involving other languages as well.

 

[DomainRM]

Absolutely, diving into the world of robotics with Python and C++ is a thrilling journey, and you're right about ROS (Robot Operating System), OpenCV, Numpy, and TensorFlow being staples in the field. Let's expand on that list and cover some other essential tools and libraries, as well as some common practices in robotics programming.

In the realm of Python, aside from what you've mentioned:

• Pandas is often used for data manipulation and analysis, crucial for handling sensor data.
• Matplotlib and Seaborn for data visualization, helping in the analysis of sensor readings and algorithm outputs.
• SciPy for scientific and technical computing, offers tools for optimization, signal processing, and more—very handy in robotics.

For C++ enthusiasts, here are some go-to libraries:

• Eigen is indeed the C++ equivalent to Numpy for linear algebra, heavily utilized in robotics for various matrix calculations and transformations.
• Boost provides free peer-reviewed portable C++ source libraries, which are widely used for their robustness and ready-to-use implementations of algorithms.
• PCL (Point Cloud Library) is extensively used in 3D perception, object recognition, and 3D reconstruction.

Regarding MoveIt, yes, it's quite popular for motion planning in robotics, integrating seamlessly with ROS and providing a comprehensive suite of tools for robotics applications.

As for programming styles and quirks:

• Event-driven programming is common in robotics to handle asynchronous events like sensor input or message passing.
• Concurrent programming practices are crucial, especially in handling multiple tasks, like reading sensors, controlling actuators, and making decisions simultaneously.
• State machines are often used for managing the various states of a robot, from initialization to task execution and error handling.

Robotics programming indeed has its nuances, like prioritizing real-time performance and dealing with the physical unpredictability of the real world, which might not be emphasized in typical programming tutorials. Developing robust error handling and safety mechanisms are also more critical in robotics than in many other fields.

Exploring these tools and concepts will give you a robust foundation for tackling various challenges in robotics. Happy coding!

 

[RinatM]

Everything robots-are-fun mentioned about Python is absolutely correct. The same goes for command line and Linux tools, which are indispensable.

Additionally, depending on your company's practices, you might need some familiarity with JavaScript/TypeScript or just a general understanding of the language used for developing the user interface, so you can make necessary adjustments.

For Python users, I would recommend gaining some proficiency with Pandas and choosing a preferred plotting library. Matplotlib, which has been a staple for a long time, is fast and lightweight, though it can be somewhat verbose. Plotly, on the other hand, allows for creating visually appealing outputs with fewer lines of code, though it's a bit more prescriptive about visual styling.