85 lines
4.0 KiB
TeX
85 lines
4.0 KiB
TeX
\chapter{Hardware and Software}
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\section{Robot}
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The aforementioned \textit{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 makes Nao an attractive development platform, suitable for the task
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of \todo{Robocup}. However, 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 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 behavior we chose the
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official NAOqi Python SDK \cite{naoqi-sdk}. Our experience with this framework
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is that it is easy to use, well documented and also covers most basic
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functionality that was necessary for us to start working on the project. A
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further advantage of this SDK is that it uses Python as the programming
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language, which allows for quick prototyping, but also makes maintaining a
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large codebase fairly easy.
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Finally, the third-party libraries that were used in the project are OpenCV and
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NumPy \cite{opencv, numpy}. OpenCV is a powerful and one of the most widely
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used open-source libraries for computer vision tasks, and NumPy is a popular
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Python library for fast numerical computations. Both of these libraries, as
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well as the NAOqi Python SDK are included in the NAOqi OS distribution by
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default, which means that no extra work was necessary to ensure their proper
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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 NAOqi C++ SDK. Being based on the C++
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language, this SDK can naturally be expected to have better performance and be
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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 ROS \cite{ros} (Robotic Operating System). ROS is a
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collection of software targeted at robot development, and there exists a large
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ecosystem of third-party extensions for ROS, which could assist in performing
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common tasks such as camera and joint calibration. ROS was an attractive
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option, but there was a major downside, that there was no straightforward way
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to run ROS locally on the robot, so the decision was made not to spend time
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trying to figure out how to do that. However, since Python is one of the main
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languages in ROS, it should be possible to incorporate our work into ROS.
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Finally, as was already mentioned in the introduction, B-Human Framework is a
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popular choice for beginners, thanks to the quality of the algorithms and good
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documentation. However, B-Human has been in development over many years and is
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therefore a very complex system. The amount time needed to get familiar with
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the code, and then to incorporate our changes would have been too big, for this
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reason we decided to use the simpler option as a starting point.
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