Difference between revisions of "Ruben-RV-ROS01"
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=== Feasible Solutions === | === Feasible Solutions === | ||
| + | The feasible solutions are Dynamic Window and Potencial Field : | ||
| + | Dynamic Window : is a velocity-based local planner that calculates the optimal collision-free ('admissible') linear and angular velocity | ||
| + | for a robot required to reach its goal | ||
| + | |||
| + | Potential-field : In the pontetial-field navigation each obstacle has an obstacle 'force field' for repelling the robot, | ||
| + | and the goal has a attraction fiel | ||
=== Dynamic window algorithm === | === Dynamic window algorithm === | ||
Revision as of 14:45, 13 October 2013
- Project name: Turtlebot robot indoor navigation
- Dates: July 2013 -
- Degree: Summer Research Residence
- Authors: Ruben
- Contact: runix404@gmail.com
- SVN Repositories:
- Tags: ROS, navigation, turtlebot
- Technologies: kinect, pcl, openni, c++, cmake, ROS
- State: Ongoing
Contents
ROS Fuerte Installation on Ubuntu 12.04
Repository configuration
First we need to add the repository
sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu precise main" > /etc/apt/sources.list.d/ros-latest.list'
and setup the keys
wget http://packages.ros.org/ros.key -O - | sudo apt-key add -
Installation
We need to refresh the cache of your package manager
sudo apt-get update
In our case, we decide to install ros-fuerte-desktop-full that is recommended in the ROS web, but you can also install other as explained on the web
sudo apt-get install ros-fuerte-desktop-full
Enviroment Configuration
Now we need to configure the enviroment variables
echo "source /opt/ros/fuerte/setup.bash" >> ~/.bashrc
For refresh the enviroment variables of your terminal you can reset it or type
. ~/.bashrc
Turtlebot installation
The turtlebot installation is very simple, you only have to type in your terminal
sudo apt-get install ros-fuerte-turtlebot*
Feasible Solutions
The feasible solutions are Dynamic Window and Potencial Field :
Dynamic Window : is a velocity-based local planner that calculates the optimal collision-free ('admissible') linear and angular velocity for a robot required to reach its goal
Potential-field : In the pontetial-field navigation each obstacle has an obstacle 'force field' for repelling the robot, and the goal has a attraction fiel
Dynamic window algorithm
Experiments
In these experiments, the robot will try to reach the position (3,3) from the origin.
1. Simple scenario Simple navigation
2. A wall scenario Navigation with obstacle
3. A realistic scenario
Path Planning
Voronoi
ongoing
Graph SLAM
ongoing
