It has been a while since i started to build an upgraded version of Dolly, my first DIY ROS based robot. This upgraded version is named Jarvis.
Changes from the previous version:
As a work in progress, I'm now writing a booklet that detail the building process of the robot both on hardware and soft software (Arduino, ROS 2), as well as some application use cases. The initial plan is:
All further updates on the booklet can be found here: https://doc.iohub.dev/jarvis/.
Ladies and gentlemen, please meet "Dolly the robot", the first version of my DIY mobile robot. My goal in this DIY project is to make a low-cost yet feature-rich ROS (Robot Operating System) based mobile robot that allow me to experiment my work on autonomous robot at home. To that end, Dolly is designed with all the basic features needed. To keep the bill of material as low as possible, i tried to recycle all of my spare hardware parts.
PhaROS is a collection of Pharo libraries that implements the ROS (Robot Operating System ) based client protocol. It allows developing robotic applications right in the Pharo environment by providing an abstract software layer between Pharo and ROS. This guide makes an assumption that readers already have some basic knowledge about ROS, if this is not the case, please check the following links before going any further on this page:
This is a demonstration of my current work on controlling robot using ROS and PhaROS. For that task, I've developed a dedicated PhaROS package that defines:
When evaluating the performance of a SLAM algorithm, quantifying the produced map quality is one of the most important criteria. Often, the produced map is compared with (1) a ground-truth map (which can be easily obtained in simulation) or (2) with another existing map that is considered accurate (in case of real world experiment where the ground-truth is not always available ).
Basically, grid maps are images, so image similarity measurement metrics can be used in this case. In this post, we consider three different metrics: Mean Square Error (MSE), K-nearest based normalized error (NE) and Structure Similarity Index (SSIM)
I've had funny time playing around with the Gazebo simulator for autonomous robot exploration. One thing I've encountered is that the odometry data provided by Gazebo is so perfect that, sometime, makes the simulation less realistic. I used a Turtlebot model as robot model in my simulations. Googling around, i didn't find any solution of adding noise to the odometry data of this robot (using the URDF file). I then decided to develop a dedicated ROS node allowing me to add some random noise to the Gazebo's odometry data.
First thing fist, we need to understand the robot motion model. There are many motion models, but in the scope of this article, we focus only on the odometry motion model. Often, odometry is obtained by integrating sensor reading from wheel encoders, it measures the relative motion of the robot between time \(t\) and \(t-1\) or \((t-1,t]\). In 2D environment, a robot pose is represented by a point \((x,y)\) and an orientation (rotation angle) \(\theta\), so the robot pose at the time \(t-1\) and \(t\) are denoted by:
This setup is performed and documented on an Ubuntu system with the following software stack:
sudo apt-get install git)
To follow this post, some basic knowledge on ROS is needed: