Discover what new features for a stock Leo Rover await you!
We’ve developed a Leo Examples package containing three new features for a stock Leo Rover. The best part is that they’re free and available to everybody, and you don’t need any prior knowledge to run them on your robot. Let’s see what the features are :)
The major feature among the 3 ones within the Leo Examples package is line follower.
A line follower feature allows a robot to autonomously follow a visual line placed on the surface using a feedback mechanism that controls the robot.
The operation of the line follower example we’ve developed for a Leo Rover lies in a neural network which we had to train. In order for our neural network model to work faster on the robot, we converted it using TensorFlow Lite. What’s characteristic for this line follower example is that it relies on not one, but two lines that designate a path for your Leo Rover to follow by itself. For the feature to work, you’ll need a Leo Rover, an insulating tape to mark a two-lined track, and the software uploaded to your robot.
To run the line follower, go through our tutorial. Once you’ve done that, your Leo Rover will autonomously navigate the route you designed.
Learn more about the Leo Rover's line follower example from this article.
Object detection is another of the novelties we’ve prepared for you.
It’s a technology related to computer vision and image processing that has to do with identifying and locating objects, such as people, buildings, or chairs, to name a few, by displaying bounding boxes around the detected items allowing to locate their position.
For the object detection example in a Leo Rover, we used a pre-trained model from the TensorFlow Lite repository. It will allow your robot to recognize a wide number of different objects listed in the COCO dataset by drawing a frame around the identified item. Along with the frame, there will appear the object’s label and the confidence parameter specified as a percentage. Depending on your preferences, the parameter values can be easily altered by manipulating the sliders so that the feature draws bounding boxes around only those objects whose confidence meets the percentage threshold you set.
Having your Leo Rover, a computer running on Linux OS or ROS, as well as some basic ROS understanding is all you need to fire this feature up. See how to do that in our tutorial.
And last but not least – the ARTag follower example.
This feature will enable your Leo Rover to detect and follow a printed ARTag. The only things you’ll need are a Leo Rover (obviously), a printed ARTag, as well as the integrated software.
The ARTag follower for a Leo Rover is based on a pre-made package from ROS for ARTag detection. It has specific parameters, which are the dimensions of 16x16 cm and ID 0. Only an ARTag with these parameters will be detected by this default feature. Of course, these parameters can be changed, provided that you dig into and tinker with the appropriate files, and modify these parameters as you see fit.
To be able to run the ARTag follower feature properly on your Leo Rover, go to this integration.
The Leo Examples package will soon be a default part of LeoOS about which you can read here.
There’s a lot of things you can do with your Leo Rover. Why don’t you check out other integrations available on our website? And we’re constantly working on new ones, so stay tuned :)