This post is about how to build an AWS Step Functions state machine and how you can use it to interact with IoT edge devices. In this case, we are sending a smoothie order to a "robot" and waiting for it to make that smoothie.
The state machine works by chaining together a series of Lambda functions and defining how data should be passed between them (if you're not sure about Lambda function, take a look at this blog post!). There's also a step where the state machine needs to wait for the smoothie to be made, which is slightly more complicated - we'll cover that later in this post.
This post is also available in video form - check the video link below if you want to follow along!
This is the second part of the "ROS2 Control with the JetBot" series, where I show you how to get a JetBot working with ROS2 Control! This is a sequel to the part 1 blog post, where I showed how to drive the JetBot's motors using I2C and PWM with code written in C++.
In this post, I show the next step in making ROS2 Control work with the WaveShare JetBot - wrapping the motor control code in a System. I'll walk through some concepts, show the example repository for ROS2 Control implementations, and then show how to implement the System for JetBot and see it running.
This post is also available in video form - check the video link below if you want to follow along!
Welcome to a new series - setting up the JetBot to work with ROS2 Control interfaces! Previously, I showed how to set up the JetBot to work from ROS commands, but that was a very basic motor control method. It didn't need to be advanced because a human was remote controlling it. However, if we want autonomous control, we need to be able to travel a specific distance or follow a defined path, like a spline. A better way of moving a robot using ROS is by using the ROS Control interfaces; if done right, this means your robot can autonomously follow a path sent by the ROS navigation stack. That's our goal for this series: move the JetBot using RViz!
The first step towards this goal is giving ourselves the ability to control the motors using C++. That's because the controllers in ROS Control requires extending C++ classes. Unfortunately, the existing drivers are in Python, meaning we'll need to rewrite them in C++ - which is a good opportunity to learn how the serial control works. We use I2C to talk to the motor controller chip, an AdaFruit DC Motor + Stepper FeatherWing, which sets the PWM duty cycle that makes the motors move. I'll refer to this chip as the FeatherWing for the rest of this article.
First, we'll look at how I2C works in general. We don't need to know this, but it helps to understand how the serial communication works so we can understand the function calls in the code better.
Once we've seen how I2C works, we'll look at the commands sent to set up and control the motors. This will help us understand how to translate the ROS commands into something our motors will understand.
The stage after this will be in another article in this series, so stay tuned!
This post is also available in video form - check the video link below if you want to follow along!
