Difference between revisions of "RoCKIn2014"

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(Robot Hardware)
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=== Robot Hardware ===
=== Robot Hardware ===
# iRobot Roomba  
# iRobot Roomba 520
# Dinamixel Arm (5x12a)
# Dinamixel Arm (5x12a)
# wood frame (yes, it is made with wood)
# wood frame (yes, it is made with wood)
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# Kinect
# Kinect
# Arduino Mega
# Arduino Mega
=== Robot Software ===
=== Robot Software ===

Revision as of 10:15, 18 October 2013

RoCKIn Camp 2014

  • Project Name:
RoCKIn 2014
  • Codename
Watermelon :D
  • Official Web Page
  • Staff:
Technical software: Fernando Casado
Technical software: Víctor Rodríguez 
Technical software: Francisco Lera
Technical hardware: Carlos Rodríguez
  • Other Information:
* Academic Year: 2013-2014
* SVN Repositories: soon	... 
* Tags: Augmented Reality, Elderly people, Tele-Assistence
* Technology: ROS, PCL, c++, svn, OpenCV, cmake, OpenGL, Qt, Aruco, 
* State: Development

Project Summary

This challenge focuses on domestic service robots. The project aims robots with enhanced networking and cognitive abilities. They will be able to perform socially useful tasks such as supporting the impaired and the elderly (one of the main goal of our group).

In the initial stages of the competition individual robots will begin by overcoming basic individual tasks, such as navigation through the rooms of a house, manipulating objects or recognizing faces, and then coordinate to handle house-keeping tasks simultaneously, some of them in natural interaction with humans.


We want to take part in RoCKIn with the platform developed during the las two years in the Catedra Telefónica-ule.

MYRABot robot.

Robot Hardware

  1. iRobot Roomba 520
  2. Dinamixel Arm (5x12a)
  3. wood frame (yes, it is made with wood)
  4. Notebook (Atom processor) (display+computer are separeted)
  5. Kinect
  6. Arduino Mega

Robot Software

  1. ROS (robot control)
  2. MYRA (C/C++, ArUCo, Qt, openCV)


We want to deploy in this robot the minimal functional abilities to be part of RoCKIn 2014.

  • Mavigation
  • Mapping
  • Person recognition
  • Person tracking
  • Object recognition
  • Object manipulation
  • Speech recognition
  • Gesture recognition
  • Cognition

Phase I: Initial Setup

Hardware Preparation

  1. Get arm power from main roomba brush

Software Preparation

As we are using ROS, we think that we can find each ability ready to deploy in the robot. In this way we are going to search and test each module to evaluate if we are going to be able to deploy in our robot.

Restriction: ROS Fuerte

Software Search

  • Navigation
  • Mapping
  • Person recognition
  • Person tracking
  • Object recognition
  • Object manipulation
  • Speech recognition
  • Gesture recognition
  • Cognition

Environment setup

Ros: Debugging Techniques

Phase II: Integration and Architecture

Non-Critical (but to-do)

  1. Android/iOS Teleoperation
  2. Desktop Qt interface
  3. Create robot model for Gazebo
  4. Create robot model for rviz (the same as Gazebo?)


  • Computer i7 processor, 8GB RAM, Nvidia (1-2 GB)
  • ASUS Xtion Pro Live Color RGB Sensor