Arreglada deriva odometría con robot detenido

.vscode/settings.json

@@ -3,5 +3,10 @@
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         "/opt/ros/melodic/lib/python2.7/dist-packages"
+    ],
+    "python.analysis.extraPaths": [
+        "/home/leon/Personal/Control_AND_Programacion_Robots/proyecto_CPR/ros_workspace/devel/lib/python2.7/dist-packages",
+        "/home/leon/Personal/Control_AND_Programacion_Robots/CPR_Practicas/ros_workspace/devel/lib/python2.7/dist-packages",
+        "/opt/ros/melodic/lib/python2.7/dist-packages"
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 }

README.md

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+# ALPHA BOT PROJECT
+![Robot_image](https://github.com/arubedaq/alpha-bot/blob/main/images/PlanteamientoGeneral.png)
+
+## Project description
+This project is based on the existing repository: https://github.com/ros-mobile-robots/diffbot. All the documentation and results of the project are available in "AlphaBotPlanner_Memoria.pdf".
+
+This project implements the entire navigation stack for an autonomous ground mobile robot:
+
+- Localization: We present some localization methods:
+    - Localization based on the "robot_localization" package (https://docs.ros.org/en/melodic/api/robot_localization/html/index.html), which fuses information from the following sensors:
+        - Model-based odometry
+        - LiDAR odometry, using the ROS package "scan_tools" (https://wiki.ros.org/scan_tools).
+        - IMU
+        - AMCL (Adaptive Monte Carlo Localization, http://wiki.ros.org/amcl) using LiDAR readings and the map    
+    Three versions are available, following the ROS REP105 standard (https://www.ros.org/reps/rep-0105.html).
+
+Ground Truth-based Localization: We use the actual robot position for debugging and some experiments.
+
+![EKF_image](https://github.com/arubedaq/alpha-bot/blob/main/images/EKF.png)
+
+- Path Planning: We have developed a path planning algorithm based on Dijkstra. This algorithm relies on information from a global cost map, so it will not take into account any possible map modifications during runtime; it will be treated as static. The planner has been programmed by ourselves, but to achieve this, we need to use a cost map based on a previously created global map using the ROS package "gmapping" (http://wiki.ros.org/gmapping).
+
+![Planner_image](https://github.com/arubedaq/alpha-bot/blob/main/images/Planner.png)
+
+- High-Level Control (Path Following): The developed high-level control algorithm is a path following algorithm based on Pure Pursuit. From it, we have made a series of specific adaptations for our configuration and have proposed some simplifications for its implementation.
+
+![PurePursuit_image](https://github.com/arubedaq/alpha-bot/blob/main/images/PurePursuit.png)
+
+Next, we present a series of graphs from a representative experiment:
+
+![Experiment_image](https://github.com/arubedaq/alpha-bot/blob/main/images/ExperimentoRepresentativo.png)
+
+
+## Tested Platforms
+This ROS package has been tested in the following environments:
+- Native Ubuntu 18 - ROS 1 Melodic
+- VirtualBox and VMware virtual machines 
+- WSL2
+
+## Installation
+### From Ubuntu
+This installation method will also work for WSL2 and virtual machines.
+
+- Prerequisites: Ubuntu 18 - ROS Melodic
+- If selected: WSL2 or virtual machine installed and running Ubuntu 18 - ROS Melodic
+
+```
+cd ~ # IT IS IMPORTANT TO CLONE REPO IN 
+git clone https://github.com/arubedaq/alpha-bot.git
+cd alpha-bot
+rosdep install --from-paths src --ignore-src -r -y
+catkin_make
+source devel/setup.sh
+roslaunch alpha_bot_simulation sim.launch
+```
+
+## Development
+This is intended as a deliverable for a university course. Therefore, probably will not receive any updated from now on. However, feel free to fork it and expand its functionalities ;)
+
+## Authors
+Isaac Rodríguez García
+
+Álvaro García Lora
+
+Sergio León Doncel
+
+Arturo Renato Úbeda Quilón
+
+
+