update introduction (#1144)

Author: AtsushiSakai

docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst

@@ -1,11 +1,13 @@
 Python for Robotics
 ----------------------
 
+This section explains the Python itself and features for Robotics.
+
 Python for general-purpose programming
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 `Python <https://www.python.org/>`_ is an general-purpose programming language developed by
-`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ in the late 1980s.
+`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ from the late 1980s.
 
 It features as follows:
 
@@ -17,7 +19,7 @@ It features as follows:
 #. Batteries included
 #. Interoperability for C and Fortran
 
-Due to these features, Python is the most popular programming language
+Due to these features, Python is one of the most popular programming language
 for educational purposes for programming beginners.
 
 Python for Scientific Computing
@@ -29,22 +31,65 @@ For example,
 
 #. High-level and interpreted features enable scientists to focus on their problems without dealing with low-level programming tasks like memory management.
 #. Code readability, rapid prototyping, and batteries included features enables scientists who are not professional programmers, to solve their problems easily.
-#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful scientific computing libraries.
+#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful and optimized scientific computing libraries.
 
-To address the more needs of scientific computing, many libraries have been developed.
+To address the more needs of scientific computing, many fundamental scientific computation libraries have been developed based on the upper features.
 
 - `NumPy <https://numpy.org/>`_ is the fundamental package for scientific computing with Python.
 - `SciPy <https://www.scipy.org/>`_ is a library that builds on NumPy and provides a large number of functions that operate on NumPy arrays and are useful for different types of scientific and engineering applications.
 - `Matplotlib <https://matplotlib.org/>`_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
 - `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
 - `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
 
-And more domain-specific libraries have been developed:
+Also, more domain-specific libraries have been developed based on these fundamental libraries:
+
 - `Scikit-learn <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
 - `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
+- `Networkx <https://networkx.org/>`_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
+- `SunPy <https://sunpy.org/>`_ is a community-developed free and open-source software package for solar physics.
+- `Astropy <https://www.astropy.org/>`_ is a community-developed free and open-source software package for astronomy.
+
+Currently, Python is one of the most popular programming languages for scientific computing.
 
 Python for Robotics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-TBD
+Scientific computation routine are very important for robotics.
+For example, matrix operation, optimization, and visualization are fundamental for robotics.
+
+Python has become an increasingly popular language in robotics due to its versatility, readability, and extensive libraries. Here's a breakdown of why Python is a great choice for robotics development:
+
+Advantages of Python for Robotics:
+
+Simplicity and Readability: Python's syntax is clear and concise, making it easier to learn and write code. This is crucial in robotics where complex algorithms and control logic are involved.
+Extensive Libraries: Python boasts a rich collection of libraries specifically designed for robotics:
+ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools.
+OpenCV: This powerful library provides tools for computer vision tasks like image processing, object detection, and motion tracking, essential for robots that perceive and interact with their environment.
+NumPy and SciPy: These libraries offer efficient numerical computation and scientific tools, enabling developers to implement complex mathematical models and control algorithms.
+Scikit-learn: This library provides machine learning algorithms, which are increasingly important in robotics for tasks like perception, planning, and control.
+Cross-Platform Compatibility: Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects.
+Large Community and Support: Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development.
+Use Cases of Python in Robotics:
+
+Robot Control: Python can be used to write control algorithms for robot manipulators, mobile robots, and other robotic systems.
+Perception: Python, combined with libraries like OpenCV, enables robots to process sensor data (camera images, lidar data) to understand their surroundings.
+Path Planning: Python algorithms can be used to plan collision-free paths for robots to navigate in complex environments.
+Machine Learning: Python libraries like Scikit-learn empower robots to learn from data and improve their performance in tasks like object recognition and manipulation.
+Simulation: Python can be used to create simulated environments for testing and developing robot algorithms before deploying them on real hardware.
+Examples of Python in Robotics:
+
+Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning.
+Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation.
+Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments.
+Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility.
+Getting Started with Python in Robotics:
+
+Learn Python Basics: Familiarize yourself with Python syntax, data structures, and programming concepts.
+Explore Robotics Libraries: Dive into libraries like ROS, OpenCV, and others relevant to your robotics interests.
+Practice with Projects: Start with small projects to gain hands-on experience, gradually increasing complexity.
+Join the Community: Engage with the robotics community through online forums, workshops, and conferences to learn from others and share your knowledge.
+In conclusion, Python's simplicity, extensive libraries, and strong community support make it an ideal language for robotics development. Whether you're a beginner or an experienced programmer, Python offers the tools and resources you need to build innovative and capable robots.
+
+Python is used for this `PythonRobotics` project because of the above features
+to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`.