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noetic

Overview

This package contains ROS nodes which enable a Leo Rover to perform a line follower task with the use of a neural network model, that had been trained on a color mask (capturing a line color from the camera).

In the package you can find default configuration, which consists of:

1. A few trained neural network models

2. A couple of predefined HSV bounds in the yaml files for catching the color mask

The package also contains nodes, scripts, and notebooks prepared for gathering and processing your training data, and training your own model, if you would like to (detailed instructions about training, your own model in the note above the contents).

It is one of a couple of packages made for showing an example usage of a stock Leo Rover.

Usage

Line follower

To run the line follower node, simply type:

roslaunch leo_example_line_follower line_follower.launch

It will run the default configuration for the line_follower node. However, you can specify your configuration by using launch arguments, which are as follows:

color_mask_file

The path to the yaml file with the HSV bounds (ROS parameters) for catching the line color.

pub_mask

The flag specifying whether or not to publish the color mask (neural network model input).

camera

The name of the topic with the camera image - you can specify which camera view you want to use for following a line.

vel

The name of the topic to which the node will publish the Twist messages.

model

The path to the neural network model.

Color mask

To run the color mask finder node, simply type:

roslaunch leo_example_line_follower color_mask.launch

Then you need to run rqt, and from the plugins choose:

1. The Image View - to see the color mask for the current HSV bounds.

2. The Dynamic Reconfigure - to choose the HSV bounds with the sliders.

After specifying your bounds, you can kill the node and it will print your configuration on the standard output.

Data saver

To run the data saver node, simply type:

roslaunch leo_example_line_follower record_data.launch

It will run the default configuration for the node. Although, you can specify your configuration, by using launch arguments, which are as follows:

duration

The information on how long the data will be recorded.

output_dir

The name of the output directory for the recorded data.

video_topic

The name of the topic with the camera image - you can specify which camera you want to record the data from.

vel_topic

The name of the topic with the rover velocity - the label for the recorded data

Preparing data

After recording the training data, you need to process it to be ready to use in the training. Therefore, the package contains a script that takes the names of the directories with the data (with partition for the training and validation data), and returns a single zip file. To run the script, simply type:

rosrun leo_example_line_follower prepare_data.py -t <train_directories> -v <valid_directories> -z <zip_file>

where:

train_directories

Are the names of the directories with the training data.

valid_directories

Are the names of the directories with the validation data.

zip_file

Is the name of the final zip file.

Therefore, an example usage of the script is:

rosrun leo_example_line_follower prepare_data.py -t train1 train2 -v val1 val2 -z dataset.zip

ROS API

line_follower

Subscribed Topics

camera/image_raw (sensor_msgs/Image)

• The image from the camera that will be provided to the neural network model.

Published Topics

cmd_vel (geometry_msgs/Twist)

• The target velocity for the rover from the neural network model output

Parameters

pub_mask (bool)

• the flag specifying whether or not the node will publish the color_mask (the model's input).

hue_max (int, default: 179)

• The maximum hue value for the color_mask.

hue_min (int, default: 0)

• The minimum hue value for the color_mask.

sat_max (int, default: 255)

• The maximum saturation value for the color_mask.

sat_min (int, default: 0)

• The minimum saturation value for the color_mask.

val_max (int, default: 255)

• The maximum val (V from HSV) value for the color_mask.

val_min (int, default: 0)

• The minimum val (V from HSV) value for the color_mask.

color_mask

Subscribed Topics

camera/image_raw (sensor_msgs/Image)

• The image from the camera that will be used for catching a color with the color mask.

Published Topics

color_mask (sensor_msgs/Image)

• A live view of the color mask caught with the currently chosen values.

catched_colors/compressed (sensor_msgs/CompressedImage)

• The live view of the colors that were caught with the color mask.

Parameters

hue_max (int, default: 179)

• The maximum hue value for the color_mask.

hue_min (int, default: 0)

• The minimum hue value for the color_mask.

sat_max (int, default: 255)

• The maximum saturation value for the color_mask.

sat_min (int, default: 0)

• The minimum saturation value for the color_mask.

val_max (int, default: 255)

• The maximum val (V from HSV) value for the color_mask.

val_min (int, default: 0)

• The minimum val (V from HSV) value for the color_mask.

data_saver

Subscribed Topics

camera/image_raw (sensor_msgs/Image)

• The camera view that will be recorded.

cmd_vel (geometry_msgs/Twist)

• The current velocity of the rover, used as labels for a recorded image