RoBorregos RoboCup @HOME 2024

Current development of RoBorregos, for the RoboCup @HOME competition, in the Open League Platform (OPL). The goal is to built a service robot capable of accomplishing tasks in domestic applications. The software areas being developed are: Human-Robot Interaction and Cooperation, Object Manipulation, Computer Vision, and Navigation and Mapping.

This repository features the task_manager package, in charge of managing Behavior Integration with all the areas. For more information on the project, check our documentation.

Setup

Each development area has its own repository, added as a submodule here. To only clone this repo run:

git clone https://github.com/Roborregos/home

And to update the submodules content execute:

git submodule update --init --recursive

All the development environments use Docker, follow the instructions below or the README inside the docker folder for insights.

Software Architecture

Docker Development

The project uses Docker for easier development within ROS and CUDA/Jetson compatibility. Both this main engine repository and each area's contain a docker folder with dockerfiles and a Makefile for easier image and container creation and modification.

Requirements

• Docker Engine
• Post-installation steps for Linux If using GPU:
• NVIDIA Driver
• NVIDIA Container Toolkit)

Container Creation

To build an image, run:

# For CPU
make main.build
# For GPU
make main.build.cuda
# For Jetson L4T: 35.4.1
make main.build.jetson

To create a container, run the following commands. The ws folder is mounted by default, and additional folders can be added with the volumes argument, with both absolute and relative paths allowed:

# For CPU
make main.create volumes="another_folder1/,~/another_folder2"
# For GPU
make main.create.cuda volumes="another_folder1/,~/another_folder2"
# For Jetson L4T: 35.4.1
make main.create.jetson volumes="another_folder1/,~/another_folder2"

To enter the container, run:

make main.up
make main.shell

You can stop and remove the container with:

make main.down
make main.remove

Additional commands can be added within the Makefile and the scripts inside the docker/scripts folder can help for easier integration and sharing. These include a build script to run the dockerfile and create a new image and a run script to create containers from it. Any additional dependency or system/environment configuration should be added to these scripts.

Task manager

The frida_task_manager is the central package in charge of processing the command information and directing the tasks with its respective area. For code readability, each area development its inside a Python module area_tasks.py, and they are called by task_manager_server.py, the script were the ROS node is created.

At the top of this mentioned file, there are four constants to enable or disable the tasks, you can change any of these to False is for the current test there is no need of an area:

NAV_ENABLED = True 
MANIPULATION_ENABLED = True
CONVERSATION_ENABLED = True 
VISION_ENABLED = False

For executing the task_manager, follow the above steps of Docker setup. Inside the bash terminal, setup the ROS network and execute the node:

export ROS_MASTER_URI=http://192.168.31.23:11311 # IP of the ROS Master (if master is in another machine)
export ROS_IP=192.168.31.97 # IP of own machine
rosrun frida_task_manager task_manager_server.py

Team Members

Name	Github	Role
Adán Flores	@afr2903	Integration, HRI & Manipulation
Emiliano Flores	@EmilianoHFlores	Integration, Manipulation & Computer Vision
Iván Romero	@IvanRomero03	Integration, HRI & Computer Vision
Alejandra Coeto	@Ale-Coeto	Computer Vision & Manipulation
Oscar Arreola	@Oscar-gg	HRI & Web
Alexis Chapa	@Chapa-1810	Manipulation & Navigation
Marina Villanueva	@mariinaVillanueva	HRI
David Vázquez	@Deivideich	Electronics, Navigation & Manipulation
Diego Hernández	@Diego-HC	Navigation
Franciso Salas	@Francisco-SP3	HRI, Navigation
Leonardo Sánchez	@LeoLFSH	Mechanics
Alex Guerrero	@alex-guerreroc	Mechanics