Linting and doc update

.github/workflows/cd.yml

@@ -12,27 +12,27 @@ jobs:
         uses: actions/checkout@v4
       - name: Free Disk Space (Ubuntu)
         uses: jlumbroso/free-disk-space@v1.3.1
-      # - name: 'CI docker base'
-      #   uses: ./.github/docker-base-action
-      #   with:
-      #     base_tag: pytorch2.2.1-cuda12.3.1-ros2humble
-      #     github_token: ${{ secrets.GITHUB_TOKEN }}
-      # - name: cleanup
-      #   run: docker system prune -a -f
-      # - name: 'CI docker base'
-      #   uses: ./.github/docker-base-action
-      #   with:
-      #     base_tag: pytorch2.2.1-ros2humble
-      #     github_token: ${{ secrets.GITHUB_TOKEN }}
-      # - name: cleanup
-      #   run: docker system prune -a -f
-      # - name: 'CI docker base'
-      #   uses: ./.github/docker-base-action
-      #   with:
-      #     base_tag: pytorch2.2.1-cuda12.3.1
-      #     github_token: ${{ secrets.GITHUB_TOKEN }}
-      # - name: cleanup
-      #   run: docker system prune -a -f
+      - name: 'CI docker base'
+        uses: ./.github/docker-base-action
+        with:
+          base_tag: pytorch2.2.1-cuda12.3.1-ros2humble
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+      - name: cleanup
+        run: docker system prune -a -f
+      - name: 'CI docker base'
+        uses: ./.github/docker-base-action
+        with:
+          base_tag: pytorch2.2.1-ros2humble
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+      - name: cleanup
+        run: docker system prune -a -f
+      - name: 'CI docker base'
+        uses: ./.github/docker-base-action
+        with:
+          base_tag: pytorch2.2.1-cuda12.3.1
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+      - name: cleanup
+        run: docker system prune -a -f
       - name: 'CI docker base'
         uses: ./.github/docker-base-action
         with:

README.md

@@ -4,7 +4,7 @@ See full documentation at [https://KumarRobotics.github.io/CoverageControl/](htt
 
 Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known _a priori_.
 The library provides a simulation environment, algorithms, and GNN-based architectures for the coverage control problem.  
-<img align="right" width="300" src="doc/graphics/LPAC.gif">
+<img align="right" width="300" src="https://github.com/KumarRobotics/CoverageControl/blob/main/doc/graphics/LPAC.gif">
 
 **Key features:**  
 - The core library `CoverageControlCore` is written in `C++` and `CUDA` to handle large-scale simulations
@@ -34,7 +34,7 @@ The library provides a simulation environment, algorithms, and GNN-based archite
 > arXiv preprint arXiv:2401.04855 (2024).
 
 
-## Open Source Libraries Dependency
+## Acknowledgements
 - [PyTorch](https://pytorch.org/)
 - [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)
 - [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page)

doc/Doxyfile

@@ -3,17 +3,20 @@
 
 PROJECT_NAME           = "Coverage Control Library"
 PROJECT_NUMBER         = $(GIT_TAG)
+EXTRACT_STATIC         = YES
 USE_MDFILE_AS_MAINPAGE = doc/manual/README.md
 INPUT                  = doc/manual/README.md \
 												 doc/manual/ref_manual.txt \
 												 doc/manual/installation.md \
 												 doc/manual/quick_start.md \
-												 doc/manual/coverage-control.md \
 												 doc/manual/lpac.md \
+												 doc/manual/coverage-control.md \
 												 cppsrc/core \
 												 params \
 												 cppsrc/main/coverage_algorithm.cpp \
-                         python/coverage_control
+                         python/coverage_control \
+                         python/scripts \
+                         python/utils
 EXCLUDE                = doc/cppsrc/torch doc/cppsrc/main/torch
 OUTPUT_DIRECTORY       = doc/
 LAYOUT_FILE            = doc/config/DoxygenLayout.xml
@@ -22,4 +25,4 @@ IMAGE_PATH             = doc/graphics
 HTML_EXTRA_FILES			+= doc/graphics/LPAC.gif doc/graphics/coveragecontrol_global.png doc/graphics/learnable_pac.png
 FILTER_PATTERNS        = "*.md=python doc/bash-filter.py" *.py=doc/py-filter.sh
 ALIASES								+= repo_owner_lower="kumarrobotics"
-ALIASES								+= docker_cr="ghcr.io/kumarrobotics/testdoc"
+ALIASES								+= docker_cr="ghcr.io/kumarrobotics/coveragecontrol"

doc/manual/README.md

@@ -41,7 +41,7 @@ The library provides a simulation environment, algorithms, and GNN-based archite
 > arXiv preprint arXiv:2401.04855 (2024).
 
 
-## External Dependencies
+## Acknowledgements
 - [PyTorch](https://pytorch.org/)
 - [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)
 - [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page)

doc/manual/coverage-control.md

@@ -1,6 +1,7 @@
-\page coverage-control-problem Problem Statement
+\page coverage-control-problem Theoretical Background
 \tableofcontents
 
+# Coverage Control Problem
 ## Introduction
 Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known _a priori_.
 The goal is to provide sensor coverage based on the importance of information at each point in the environment.
@@ -63,3 +64,38 @@ In such a setting, a coverage control algorithm needs to provide the following b
 Designing such decentralized algorithms is challenging and can be intractable for complex systems.
 This motivates us to use a learning-based approach to design a decentralized coverage control algorithm.
 The \ref lpac with GNN addresses the above challenges and provides a scalable and robust solution to the problem.
+
+------
+
+# LPAC Architecture
+
+## Navigation of Robot Swarms
+Navigating a swarm of robots through an environment to achieve a common collaborative goal is a challenging problem, especially when the sensing and communication capabilities of the robots are limited.
+These problems require systems with high-fidelity algorithms comprising three key capabilities: perception, action, and communication, which are executed in a feedback loop, i.e., the Perception-Action-Communication (PAC) loop.
+To seamlessly scale the deployment of such systems across vast environments with large robot swarms, it is imperative to consider a decentralized system wherein each robot autonomously makes decisions, drawing upon its own observations and information received from neighboring robots.
+
+## The Challenge
+Designing a navigation algorithm for a decentralized system is challenging.
+The robots perform perception and action independently, while the communication module is the only component that can facilitate robot collaboration.
+Under limited communication capabilities, the robots must decide _what_ information to communicate to their neighbors and _how_ to use the received information to take appropriate actions.
+The motivation of designing this library is to study the coverage control problem as a canonical problem for the decentralized navigation of robot swarms.
+We develop the learnable PAC (LPAC) architecture that can learn to process sensor observations, communicate relevant information, and take appropriate actions.
+
+## Architecture
+The learnable Perception-Action-Communication (LPAC) architecture is composed of three different types of neural networks, one for each module of the PAC system.
+1. In the perception module, a convolution neural network (CNN) processes localized IDF observations and generates an abstract representation.
+2. In the communication module, a GNN performs computation on the output of the perception module and the messages received from neighboring robots.
+It generates a fixed-size message to communicate with the neighbors and aggregates the received information to generate a feature vector for the action module of the robot.
+3. In the action module, a shallow multilayer perceptron (MLP) predicts the control actions of the robot based on the feature vector generated by the GNN.
+
+\htmlonly
+<img class="center" style="width: 80%; margin-left: auto; margin-right: auto;" src="learnable_pac.png"/>
+<figcaption>Learnable Perception-Action-Communication (LPAC) architecture:
+The three modules are executed on each robot independently, with the GNN in the communication module facilitating collaboration between robots.
+</figcaption>
+\endhtmlonly
+
+> [LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control.](https://doi.org/10.48550/arXiv.2401.04855)  
+> Saurav Agarwal, Ramya Muthukrishnan, Walker Gosrich, Vijay Kumar, and Alejandro Ribeiro.  
+> arXiv preprint arXiv:2401.04855 (2024).
+