feat(docs_ws): Update documentation.

docs_ws/src/codebase/codebase_overview.rst

@@ -0,0 +1,5 @@
+.. _codebase_overview_link:
+
+CodeBase Overview
+=================
+

docs_ws/src/codebase/hydra_ws.rst

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+Hydra Workspace
+===============
+
+.. note::
+
+    This workspace is primarily adapted from Hydra. 
+    For more detailed information, please refer to the `original github repository <https://github.com/MIT-SPARK/Hydra>`_ or read the `paper <https://arxiv.org/abs/2201.13360>`_.

docs_ws/src/codebase/kobuki_ws.rst

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+Kobuki Workspace
+================

docs_ws/src/codebase/llm_query_ws.rst

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+LLM Query Workspace
+===================

docs_ws/src/codebase/realsense_ws.rst

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+Realsense Workspace
+===================

docs_ws/src/codebase/ros1_bridge_ws.rst

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+ROS1 Bridge Workspace
+=====================

docs_ws/src/codebase/vlplidar_ws.rst

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+VLP-16 LiDAR Workspace
+=======================

docs_ws/src/demo/demo.rst

@@ -1,5 +1,22 @@
 Demo
 ====
 
-Youtube
--------
+Gazebo
+-------
+
+.. raw:: html
+
+    <iframe width="615" height="352" src="https://www.youtube.com/embed/1j9gkKwj_IY"
+        title="Demo for Pervasive AI Developer Contest with AMD" frameborder="0"
+        allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+        referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+Habitat Sim
+------------------
+
+.. raw:: html
+
+    <iframe width="615" height="352" src="https://www.youtube.com/embed/fI6xyzNm0TY"
+        title="【Hololive 歌曲 / Gawr Gura 唱歌】Rick Astley - Never Gonna Give You Up「中文字幕」" frameborder="0"
+        allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+        referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

docs_ws/src/index.rst

@@ -21,18 +21,36 @@ Links
 
    introduction/introduction
    introduction/architecture
-   introduction/how_to_use
+   introduction/ros2
+   introduction/quick_start
 
 .. toctree::
    :caption: Installation
    :maxdepth: 2
    :numbered:
 
-   installation/installation
+   installation/docker
+   installation/devcontainer   
+   installation/ros2
+
+.. toctree::
+   :caption: CodeBase
+   :maxdepth: 2
+   :numbered:
+
+   codebase/codebase_overview
+   codebase/hydra_ws
+   codebase/kobuki_ws
+   codebase/llm_query_ws
+   codebase/realsense_ws
+   codebase/ros1_bridge_ws
+   codebase/vlplidar_ws
 
 .. toctree::
    :caption: Demo
    :maxdepth: 2
    :numbered:
 
-   demo/demo
+   demo/demo
+
+If you have any questions, please contact the author by email: ``yuzhong1214@gmail.com``, or create an issue on the `GitHub repository <https://github.com/YuZhong-Chen/LLM-Navigation>`_.

docs_ws/src/installation/devcontainer.rst

@@ -0,0 +1,16 @@
+Devcontainer
+============
+
+.. note::
+
+    For more information about installation, please visit the official `devcontainer website <https://code.visualstudio.com/docs/devcontainers/tutorial#_install-the-extension>`_.
+
+Press the link below to install the Devcontainer extension:
+
+`Install the Devcontainer extension <vscode:extension/ms-vscode-remote.remote-containers>`_
+
+Or you can install it from the Visual Studio Code Marketplace:
+
+.. image:: ./images/devcontainer.png
+    :align: center
+    :alt: Devcontainer Extension

docs_ws/src/installation/docker.rst

@@ -0,0 +1,44 @@
+Docker
+======
+
+.. note::
+
+    For more information about installation, please visit the official `docker website <https://docs.docker.com/engine/install/ubuntu/>`_.
+
+To ensure better maintainability of our code, we have packaged all the necessary packages and dependencies into Docker and split it into multiple workspaces based on functionality. 
+Normally, you only need to navigate to the designated workspace and use `docker compose up` to complete the entire environment setup.
+
+Follow these steps to install Docker and Docker Compose on your system:
+
+
+1. **Update your package index**::
+
+    sudo apt-get update
+
+2. **Install required packages**::
+    
+    sudo apt install apt-transport-https ca-certificates curl software-properties-common
+
+3. **Add the Docker GPG key**::
+    
+    sudo install -m 0755 -d /etc/apt/keyrings
+    sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
+    sudo chmod a+r /etc/apt/keyrings/docker.asc
+
+4. **Add the Docker repository**::
+
+    echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
+        $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
+        sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
+
+5. **Update your package index again**::
+
+    sudo apt-get update
+
+6. **Install Docker**::
+
+    sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
+
+7. **Verify the installation**::
+
+    sudo docker --version

docs_ws/src/installation/ros2.rst

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+ROS2 Humble
+===========
+
+.. note::
+
+    For more information about installation, please visit the official `ROS2 website <https://docs.ros.org/en/humble/Installation/Ubuntu-Install-Debs.html>`_.
+
+Please note that we have already installed ROS 2 Humble within the Docker environment. 
+Generally, you do not need to install ROS 2 yourself. You will only need to manually install ROS 2 in a few exceptional cases, 
+such as when you want to add features to our codebase but have your own Dockerfile.
+
+Follow these steps to install ROS 2 Humble on your system:
+
+1. **Set Up Your System**
+    Ensure that your system is up to date::
+
+        sudo apt update
+        sudo apt upgrade
+        sudo apt install -y software-properties-common
+        sudo add-apt-repository universe
+
+2. **Add the ROS2 GPG Key**  
+    Download and add the GPG key for the ROS 2 repository::
+
+        sudo apt install -y curl
+        sudo curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg
+
+3. **Add the ROS 2 Repository**  
+    Add the ROS 2 repository to your sources list::
+
+        echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo $UBUNTU_CODENAME) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null
+
+
+4. **Install ROS 2 Humble**  
+    Update your package list and install the full desktop version of ROS 2 Humble::
+
+        sudo apt update
+        sudo apt upgrade
+        sudo apt install ros-humble-desktop
+
+
+5. **Set Up Environment Variables**  
+    Add the ROS 2 environment variables to your shell::
+
+        echo "source /opt/ros/humble/setup.bash" >> ~/.bashrc
+        source ~/.bashrc

docs_ws/src/introduction/architecture.rst

@@ -4,5 +4,27 @@ Architecture
 Software
 --------
 
+.. image:: ./images/software_arch.png
+    :align: center
+    :alt: Software Architecture
+
+Our software architecture consists of four main modules. The low-level control module receives goal points and control signals to direct the robot's movements. 
+The segmentation module processes RGBD images from the robot's sensors (camera, LiDAR, odometry) and identifies different objects and regions in the environment. 
+The scene graph (SG) builder module constructs a 3D scene graph from the segmented data, representing spatial relationships and object attributes. 
+Finally, the language query module, powered by a large language model, interprets high-level instructions and converts them into goal points for the low-level controller, 
+enabling the robot to understand and execute complex tasks. Later, we will introduce our implementation details in the following paragraph.
+
 Hardware
---------
+--------
+
+.. image:: ./images/hardware_arch.png
+    :align: center
+    :alt: Hardware Architecture
+
+In our hardware architecture, various components work together to enable the robot to navigate and interact with its environment efficiently. 
+The AMD KR260 serves as the core component, interfacing with multiple sensors including the Realsense camera, VLP16 LiDAR, and the Kobuki wheeled robot. 
+The KR260 processes input from these sensors, utilizing SLAM to construct a basic 2D costmap for navigation and building a 3D scene graph from labeled images. 
+Due to the high computational load, a local server is employed for semantic segmentation, 
+sending the labeled images back to the KR260 to build a semantic 3D scene graph. The scene graph is encoded into a hierarchical YAML file, 
+which is then queried by a remote server hosting a Large Language Model (LLM) with goal descriptions such as "I want to go to bed." 
+The LLM processes these queries and returns a goal point, which the KR260 uses to navigate the robot to the desired location.

docs_ws/src/introduction/introduction.rst

@@ -16,12 +16,34 @@ the agent is tasked to navigate to the specified goal location using its forward
 
     Pipeline
 
+Research in 3D scene graphs (3DSG) offers valuable insights for our mapping approach. 
+3DSG can hierarchically organize multiple levels of semantic meanings as nodes, including floors, places, and objects. 
+The edges in a 3DSG can represent relationships between objects and the traversability between locations. 
+Additionally, the real-time 3DSG construction approach presented by Hydra suggests the feasibility of integrating 3D scene graphs into our navigation task.
+
+This detailed semantic structure can serve as a mental map for the agent, facilitating query-to-point reasoning. 
+For example, given a goal query like “I want to go to sleep, where should I go?” and the current 3D scene graph, 
+the LLM can guide the agent towards the appropriate point.
+
+There are 3 main components in our architecture: 
+
+- The AMD KR260 acts as a core component that processes the inputs from several sensors, and updating the 3D scene graph. 
+- A local server response for semantic segmentation module. 
+- A LLM to reason the 3DSG based on the given query, and output the location the agent should go to.
+
+Compared to Hydra, our approach supports processing language queries, different from traditional navigation pipeline, 
+which should input a specific goal by user, our pipeline significantly enhancing the flexibility of the user interface. 
+Moreover, Hydra's semantic segmentation method will limit its scene understanding capabilities across diverse environments.
+
+Initially, we aimed to address our navigation task using Reinforcement Learning (RL) methods 
+due to their proven performance in obstacle avoidance and their ability to learn from various inputs 
+(in our case, RGBD images, natural language, and the 3DSG). However, we found that RL methods are unstable during training, 
+largely due to the sparsity of reward signals, and the noisy nature of real-world observations. Consequently, 
+we transitioned from RL methods to 3DSG-based methods.
 
 Features
 --------
 
-- Automatically navigate the robot to a specific goal without any high-cost sensors.
-- Based on a single camera and use deep learning methods.
-- Use Sim-to-Real technology to eliminate the gap between virtual environment and real world.
-- Introduce Virtual guidance to entice the agent to move toward a specific direction.
-- Use Reinforcement learning to avoid obstacles while driving through crowds of people.
+- Real-Time 3D scene graph construction
+- Automatically navigate the robot to a specific goal with the help of the 3D scene graph
+- Navigate to the goal location based on the user's natural language instructions

docs_ws/src/introduction/quick_start.rst

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+Quick start
+===========
+
+Before you start, make sure you have read the :ref:`codebase_overview_link` section.
+
+1. Clone the repository ...
+

docs_ws/src/introduction/ros2.rst

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+ROS2
+============
+
+For more information about ROS2, please visit the official ROS2 website: `ROS2 Humble <https://docs.ros.org/en/humble/index.html>`_.
+
+What is ROS?
+------------
+
+ROS (Robot Operating System) is a flexible framework designed to build robot applications. 
+It enables developers to create complex robotics systems by providing tools and libraries for building, testing, and deploying robotic software. 
+As an evolution of ROS (ROS1), ROS2 introduces major improvements, focusing on scalability, real-time performance, and improved communication mechanisms.
+
+Why Use ROS2?
+-------------
+
+ROS2 offers a robust platform for those developing both simple and complex robot applications. 
+It is especially useful in environments that require distributed systems, real-time control, or that use heterogeneous hardware.
+
+Key reasons to adopt ROS2 include:
+
+- **Cross-platform compatibility**: Works on Linux, Windows, and macOS.
+- **Modularity**: Provides a component-based architecture, allowing developers to reuse existing modules and customize them for their needs.
+- **Scalability**: Capable of handling large-scale distributed systems across multiple machines.
+- **Real-time capabilities**: ROS2 is designed with real-time communication in mind, enabling more predictable and timely responses.
+- **Flexible communication layers**: ROS2 supports different communication middleware, such as DDS (Data Distribution Service), making it more versatile for various applications.
+
+ROS2 is ideal for robotics researchers, hobbyists, and companies building robotic systems in diverse industries such as agriculture, healthcare, logistics, and manufacturing.  
+It is particularly well-suited for:
+
+- Developers building real-time systems.
+- Teams working with distributed robotics applications.
+- Anyone needing a scalable platform that supports multiple robots and machines working in sync.
+
+Core Features of ROS2
+----------------------
+
+1. **Node-based Architecture**  
+    ROS2 applications are divided into *nodes*, where each node performs a specific task. 
+    These nodes communicate with each other using topics, services, or actions, providing a clear and maintainable structure for building complex systems.
+
+2. **Data Distribution Service (DDS) for Communication**  
+    ROS2 uses DDS, a standardized middleware, to ensure reliable communication between nodes, 
+    whether they are on the same device or distributed across multiple machines. 
+    DDS offers better performance for real-time systems and is capable of handling high-volume data transfers, crucial for robotics applications.
+
+3. **Real-Time Systems**  
+    Unlike ROS1, ROS2 can meet real-time constraints, making it suitable for applications that require fast and deterministic responses, 
+    such as autonomous vehicles, drones, and industrial robots.
+
+4. **Multi-Robot Support**  
+    ROS2 is designed to support multiple robots operating together in the same environment. 
+    This is achieved by leveraging DDS, which allows communication across multiple networks, making it easy to scale up robot fleets.
+
+Basic ROS2 CLI Commands
+------------------------
+
+To interact with ROS2, you can use several command-line interface (CLI) commands. Here are some fundamental commands to get you started:
+
+1. **Listing Topics**  
+    To see all active topics: ``ros2 topic list``
+
+2. **Echoing Topic Data**  
+    To view messages published on a specific topic: ``ros2 topic echo <topic_name>``
+
+3. **Publishing to a Topic**  
+    To publish messages to a specific topic: ``ros2 topic pub <topic_name> <message_type> "<message_data>""``
+
+4. **Launching the Node by Launch File**
+    To start a set of nodes defined in a launch file: ``ros2 launch <package_name> <launch_file>``
+
+5. **Listing Nodes**  
+    To see all active nodes: ``ros2 node list``
+
+6. **Inspecting Node Information**  
+    To get information about a specific node: ``ros2 node info <node_name>``