README.asciidoc

rabbitamr_navigation

Contains map, config and launch files to run navigation algorithms

Executable commands :

roslaunch rabbitamr_navigation rabbitamr_amcl.launch
roslaunch rabbitamr_navigation rabbitamr_karto.launch
roslaunch rabbitamr_navigation rabbitamr_gmapping.launch
roslaunch rabbitamr_navigation rabbitamr_hector.launch

• Used nodes:

  • amcl

  • move_base

  • slam_gmapping

  • slam_karto

  • hector_slam

amcl

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach which uses a particle filter to track the pose of a robot against a known map.

Tutorial

After starting simulation or real robot run the following commands on separate terminals:

# start amcl node
roslaunch rabbitamr_navigation rabbitamr_amcl.launch

# to visualize the robot in rviz
roslaunch rabbitamr_viz rabbitamr_amcl.launch

Config

In order to customize amcl a config file is needed:

• config/amcl.yaml

move_base

The move_base package provides an implementation of an action that, given a goal in the world, will attempt to reach it with a mobile base. The move_base node links together a global and local planner to accomplish its global navigation task.

Config

In order to customize move_base several config files are needed:

• config/move_base.yaml

  • params link : http://wiki.ros.org/move_base

• config/costmap_common.yaml

  • params link : http://wiki.ros.org/costmap_2d

• config/costmap_local.yaml

  • params link : http://wiki.ros.org/costmap_2d

• config/costmap_global.yaml

  • params link : http://wiki.ros.org/costmap_2d

• config/planner_dwa.yaml

  • params link : http://wiki.ros.org/dwa_local_planner

• config/planner_navfn.yaml

  • params link : http://wiki.ros.org/navfn

• config/planner_global.yaml

  • params link : http://wiki.ros.org/global_planner

• config/planner_trajectory.yaml

  • params link : http://wiki.ros.org/base_local_planner

• config/planner_teb.yaml

  • params link : http://wiki.ros.org/global_planner

slam_gmapping

A highly efficient particle filer to learn grid maps from laser range data.

Tutorial

After starting simulation or real robot run the following commands on separate terminals:

# start slam gmapping
roslaunch rabbitamr_navigation rabbitamr_gmapping.launch

# to visualize the map in rviz
roslaunch rabbitamr_viz rabbitamr_mapping.launch

# to control the robot
roslaunch rabbitamr_control teleop.launch

# to save the map
rosrun map_server map_saver -f path/to/file

Config

In order to customize gmapping a config file is needed:

• config/slam_gmapping.yaml

slam_karto

A highly efficient particle filer to learn grid maps from laser range data.

Tutorial

After starting simulation or real robot run the following commands on separate terminals:

# start slam karto node
roslaunch rabbitamr_navigation rabbitamr_karto.launch

# to visualize the map in rviz
roslaunch rabbitamr_viz rabbitamr_mapping.launch

# to control the robot
roslaunch rabbitamr_control teleop.launch

# to save the map
rosrun map_server map_saver -f path/to/file

Config

In order to customize gmapping a config file is needed:

• config/slam_karto.yaml

slam_hector

A highly efficient particle filer to learn grid maps from laser range data.

Tutorial

After starting simulation or real robot run the following commands on separate terminals:

# start karto node
roslaunch rabbitamr_navigation rabbitamr_hector.launch

# to visualize the map in rviz
roslaunch rabbitamr_viz rabbitamr_mapping.launch

# to control the robot
roslaunch rabbitamr_control teleop.launch

# to save the map
rosrun map_server map_saver -f path/to/file

Config

In order to customize gmapping a config file is needed:

• config/slam_hector.yaml