> For the complete documentation index, see [llms.txt](https://huang-jason.gitbook.io/ros/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://huang-jason.gitbook.io/ros/pkg-navigation.md).

# PKG - navigation

### 2D navigation stack

input = odometry, sensor(avoid obstacles), and a goal pose

output = safe velocity commands

![](https://548312962-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lq83PzYaksJkirwod4I%2F-Lq83Un_qn4pNS1_E1_u%2F-Lq83ZK7BUwFgcMxFzPx%2Foverview_tf_small.png?generation=1569962740291228\&alt=media)

tf = determine the robot's location in the world and relate sensor data to a static map. (**no velocity data**)

odometry = publish both a transform and a nav\_msgs/Odometry message that ***contains velocity*** information.

### HW Requirements

1. for both differential drive and holonomic wheeled robots only. command = Vel\_x, Vel\_y, Vel\_theta.
2. planar laser mounted, for map building and localization.

### costmaps

This uses 2 costmaps to store information about obstacles in the world, and in total 3 maps

global = long-term plans

local = local planning and obstacle avoidance

common = global & local

<http://wiki.ros.org/costmap_2d>

## costmap\_common\_params.yaml

```yaml
# obstacle information
obstacle_range: 2.5
raytrace_range: 3.0

# robot body
footprint: [[x0, y0], [x1, y1], ... [xn, yn]]
#robot_radius: ir_of_robot
inflation_radius: 0.55

# source
observation_sources: laser_scan_sensor point_cloud_sensor
laser_scan_sensor: {sensor_frame: frame_name, data_type: LaserScan, topic: topic_name, marking: true, clearing: true}
point_cloud_sensor: {sensor_frame: frame_name, data_type: PointCloud, topic: topic_name, marking: true, clearing: true}
```

#### obstacle\_range: 2.5

The robot will only update its map with info about obstacles that are within 2.5 meters of the base.

#### raytrace\_range: 3.0

The robot will attempt to clear out space in front of it up to 3.0 meters away given a sensor reading.

#### footprint: \[\[x0, y0], \[x1, y1], ... \[xn, yn]]

center of the robot is assumed to be at (0.0, 0.0)

#### inflation\_radius: 0.55

the robot will treat all paths that stay 0.55 meters or more away from obstacles as having equal obstacle cost.

### global\_costmap\_params.yaml

```yaml
global_costmap:
  global_frame: /map
  robot_base_frame: base_link
  update_frequency: 5.0
  static_map: true
```

#### global\_frame

defines what coordinate frame the costmap should run in

#### robot\_base\_frame

defines the coordinate frame the costmap should reference for the base of the robot.

#### static\_map

If you aren't using an existing map or map server, set the static\_map parameter to false.

### local\_costmap\_params.yaml

```yaml
local_costmap:
  global_frame: odom
  robot_base_frame: base_link
  update_frequency: 5.0
  publish_frequency: 2.0
  static_map: false
  rolling_window: true
  width: 6.0
  height: 6.0
  resolution: 0.05
```

#### rolling\_window

true means that the costmap will remain centered around the robot as the robot moves through the world.

#### width (meters), height (meters), and resolution (meters/cell)

dimension of the costmap

### base\_local\_planner

given a high-level plan, compute velocity commands and send to the robot.

```yaml
TrajectoryPlannerROS:
  max_vel_x: 0.45
  min_vel_x: 0.1
  max_vel_theta: 1.0
  min_in_place_vel_theta: 0.4

  acc_lim_theta: 3.2
  acc_lim_x: 2.5
  acc_lim_y: 2.5
  holonomic_robot: true
```

<http://wiki.ros.org/base_local_planner>