86 lines
4.1 KiB
TeX
86 lines
4.1 KiB
TeX
\chapter{Hardware and Software}
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\section{Robot}
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The aforementioned Nao \cite{nao} is a small humanoid robot, around 60
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cm tall. Some of its characteristics are:
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\begin{itemize}
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\item Two HD-cameras on the head
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\item An ultrasonic rangefinder on the body
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\item An inertial navigation unit (accelerometer and gyroscope)
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\item Internet connectivity over Ethernet cable or 802.11g WLAN
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\item Single-core Intel Atom CPU and 1 GB of RAM
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\item Programmable joints with overall 25 degrees of freedom
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\item Speakers
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\item 60 to 90 minutes battery life
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\end{itemize}
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It can be seen from the specifications list, that the multitude of sensors and
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interfaces make the Nao an attractive development platform, suitable for the task
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of playing soccer. However, a relatively weak CPU and a low amount of RAM require
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the programs running on the robot to be resource-efficient, which had to be
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taken into account during our work on the project.
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\section{Software}
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In our project we used \textit{NAOqi OS} as an operating system for the robot.
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This is a standard operating system for Nao robots based on Gentoo Linux and
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it can handle all aspects of robot control, such as reading the sensors, moving
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the robot and establishing the network connection.
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As a framework for the implementation of the desired behaviour we chose the
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official \textit{NAOqi Python SDK} \cite{naoqi-sdk}. We found this framework
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easy to use, well documented and also covering most basic functionality that
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was necessary for us to start working on the project. A further advantage of
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this SDK is that it uses Python as the programming language, which allows for
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quick prototyping, but also makes maintaining a large codebase fairly easy.
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Finally, the third-party libraries that were used in the project are
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\textit{OpenCV} and \textit{NumPy} \cite{opencv, numpy}. OpenCV is a powerful
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and one of the most widely used open-source libraries for computer vision
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tasks, and NumPy is a popular Python library for fast numerical computations.
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Both of these libraries, as well as the NAOqi Python SDK are included in the
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NAOqi OS distribution by default, which means that no extra work was necessary
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to ensure their proper functioning on the robot.
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\section{Rejected Software Alternatives}
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Here we will briefly discuss what alternative options were available for the
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choice of the base framework, and why we decided not to use those. One
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available option was the official \textit{NAOqi C++ SDK}. Being based on the
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C++ language, this SDK can naturally be expected to have better performance and
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be more resource-efficient, than the Python-based version. We still chose the
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Python SDK, because C++ is not particularly suitable for fast prototyping,
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because of the complexity of the language. It is also worth noting, that we
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never really hit the performance constraints, that couldn't have been overcome
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by refactoring our code, but in the future it might be reasonable to migrate
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some of the portions of it to C++.
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Another big alternative is \textit{ROS} \cite{ros} (Robotic Operating System).
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ROS is a collection of software targeted at robot development, and there exists
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a large ecosystem of third-party extensions for ROS, which could assist in
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performing common tasks such as camera and joint calibration, as well as in
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more complex tasks such as object detection. ROS was an attractive option, but
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there was a major downside, that there was no straightforward way to run ROS
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locally on the robot, so the decision was made not to spend time trying to
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figure out how to do that. However, since Python is one of the main languages
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in ROS, it should be possible in the future to incorporate our work into ROS.
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Finally, as was already mentioned in the introduction, \textit{B-Human
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Framework} is a popular choice for beginners, thanks to the quality of the
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algorithms and good documentation. However, B-Human has been in development
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over many years and is therefore a very complex system. The amount time needed
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to get familiar with the code, and then to incorporate our changes would have
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been too big, for this reason we decided to use the simpler option as a
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starting point.
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