dcocoma/frm-g5

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1. Identify the project type and purpose

- This is an academic “mono‑repo” for a Mobile Robotics course. It contains six independent lab assignments (Lab1…Lab4, plus Lab2a, Lab2b, Lab2c, Lab3).

- Each lab is a self‑contained exercise demonstrating robotics topics: basic algorithms (Lab1), ROS nodes (Lab2a), Arduino & EV3 integration (Lab2b), simple scripts (Lab2c), sensor data processing with MATLAB (Lab3), and EV3 mission planning (Lab4).

2. Analyze the file structure

- Top‑level directories: Lab1, Lab2a, Lab2b, Lab2c, Lab3, Lab4. Each has subfolders “Code” or “src”, “Imgs”, sometimes “Datos” or “Vids”.

- Lab2a: a full ROS/Catkin workspace with src/pruebas (C++ & Python nodes), build/, devel/ and launch files—classic ROS node/topic architecture.

- Lab2b: Arduino sketches (EV3Test, Main, ROSTest), ros_lib.zip, an ev3_arduino Catkin package with Python HMI.

- Lab3: MATLAB code modules for LIDAR & ultrasound, plus CSV data.

- Lab4: EV3 mission scripts (.lmsp).

- Common pattern: each lab is an independent “service” or component.

3. Examine the README for additional insights

- The README only establishes authors and that this is a lab‑practice repository for “Fundamentos de Robótica Móvil” (2024‑I).

4. How to create the system design diagram

a. Main components

• Monorepo root

• Lab1…Lab4 modules

• For Lab2a: ROS Master, C++ nodes (hello, pubvel, subpose), Python nodes (pypubvel.py, pysubpose.py, triangle.py, turtlesimservice.py), message topics.

• For Lab2b: EV3 hardware + Arduino sketches, rosserial bridge, ros_lib interface, Python HMI.

• For Lab3: Sensors (LIDAR, ultrasound), MATLAB scripts, Data CSV input, Plots/Outputs.

• For Lab4: EV3 robot missions.

b. Relationships & interactions

• Lab2a: all ROS nodes register with ROS Master, publishers and subscribers exchanging geometry_msgs/Twist or custom msgs.

• Lab2b: EV3 runs Arduino firmware, communicates via rosserial node over USB/Wi‑Fi to ROS network; Python HMI issues commands over ROS topics/services.

• Lab3: MATLAB scripts read CSV → process scans → output plots.

• Lab1, Lab2c, Lab4 are standalone exercises with no runtime interactions between labs.

c. Architectural patterns & principles

• Lab2a/2b: publisher/subscriber (ROS); separation of concerns by language (C++ vs Python).

• Lab3: data‑processing pipeline.

• All labs: modules/packages with resources (Imgs, Datos, Vids).

d. Key technologies & frameworks

• ROS (Catkin build, CMake), rosserial, C++ (roscpp), Python rospy, MATLAB (.m, .mlx), EV3‑Arduino (.ino), EV3 missions (.lmsp).

5. Tailoring guidelines by project type

- Full‑stack analogy: treat each lab as a micro‑service behind a “Monorepo API Gateway” (the root). Emphasize each lab’s internal architecture.

- Open‑source tool style: highlight entrypoints (e.g., Lab2a/CMakeLists.txt, Lab2b/Arduino sketches), extension points (new nodes, new sensors), and integration with ROS.

- Data pipeline style (Lab3): show chronological stages: Data Ingest → Pre‑processing → Visualization.

- Embedded/hardware style (Lab2b, Lab4): show flow from hardware (EV3) → firmware (.ino) → middleware (rosserial) → application (Python HMI).

6. Diagramming elements

- Labels: “Monorepo root”, each “LabX module”, “ROS Master”, “Node: hello”, “Arduino EV3Test”.

- Arrows: indicate data/topic flow in ROS, file dependencies (src → build → devel), sensor data into MATLAB.

- Color coding & shapes:

• Blue rectangles for software modules (code directories).

• Green cylinders for data stores (CSV files).

• Orange circles for processes/nodes.

• Purple hexagons for hardware components (EV3 brick, LIDAR sensor).

• Dashed lines for build/deploy processes (CMake/Catkin).

7. Detailed yet modular

- Group related files into a single component box (e.g., “Lab2a ROS Workspace” containing src/, build/, devel/).

- Break out only those subcomponents that have runtime interaction (e.g., publisher node, subscriber node, ROS Master).

- Keep each lab visually separate but show common conventions (all labs under the same repo).

- Avoid over‑splitting—only show directories that represent logical components or services.

Final advice

- Start by sketching a top‑level “Monorepo” box containing each Lab folder.

- Expand Lab2a and Lab2b into sub‑diagrams showing ROS architecture.

- Use consistent shapes and color codes across labs.

- Annotate arrows with message types or data formats (e.g., CSV, Twist messages).

- Include a legend explaining shape/color semantics.

- Ensure every component is labeled, data flows are clear, and build/deploy boundaries are indicated.

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