As partner of ASUS the Leap Motion uses the same emitted and reflected infrared light for tracking parts of the human body like the Asus Xtion Pro . Available since July 2013, the Leap Motion with about 90EUR is an inexpensive, but limited input device, which is optimized for tracking fingers and hands as illustrated in the following illustrations.
LeapMotion Visualization API allows track- ing of two hands and advanced gesture recognition
LeapMotion device emits infrared light, which can be seen with a non-filtered camera
The main features include the tracking of two simultaneous hands with gesture recognition for all ten fingers. For distances between 10cm and 1m at daylight the device works reliably.
During my thesis, I have tested the existing ROS driver, which currently only supports one hand and was not able to provide 3D PointCloud data. In brief, the Leap Motion unfortunately is inappropriate for our project as their only use could be unreliable robot control by hand gestures.
Applications in robotics need to solve a lot of computational intensive tasks. While some of them can be outsourced to an externally powered device like a laptop or a server, others essentially can be calculated on the UGV.
Examples for that are collecting sensor data, receiving and executing commands or streaming data. Balancing these is a challenging task, because on concurrent executing systems all processes can influence each other. Especially when computational power gets cut down to the limits in order to save energy. As most libraries, frameworks or software environments do, ROS requires additional resources when being compared to a single purpose application.
In conclusion providing enough computational power while using reasonable amounts of energy is an important task to solve.
Physical dimensions and requirements result from a tradeoff between costs and size, whereas smaller UGVs tend to be more expensive and complex. On the other hand, an upper bound among others is set by being manageable in terms of transport and storage.
The low cost target UGV is a four wheel or two tracks driven ground robot with physical dimensions below 150mm * 300mm * 300mm (height, width, length). The drive power should be accordingly with an effective force of more than 100 Ncm for moving or holding torque in case of stronger slope. Additionally, tracks are the preferred primary propulsion system as they have better grip properties and only require simple motor control. Another nice to have would be the capability of spot-turning, which would allow operating on small areas and facilitates 3D scans of rooms without moving further than required. Another optional point if the robot is going to be used outside of buildings or around kids is a splash-proof case that would increase the robots life. Furthermore, modular extensibility would increase the usability of the robot significantly.
In order to solve the tremendous requirements of robotics in a low cost context, we need to think out of the box while structuring the challenges in solvable problems. Like the following graph shows, we should divide the functionality of UGVs into four main modules: First, Sensors are the parts the robot requires to sense the outside world, next Accumulators serving and saving power, followed by Processors the units are processing information gathered by Sensors and finally, Actuators which provide physical movement. These areas in turn get separated into further sections which we discuss one by one on the next posts.
UGVs like they are found in industry, education or Do it yourself (DIY) communities are currently not affordable for average technique enthusiasts, teachers in schools or sometimes even universities. The concept of low cost robots tries to solve that issue.
What is low cost in a robotic context?
The traditional interpretation of low cost is minimizing the expenses while keeping most important features. In borders of mostly expensive robotics this term needs to follow the same differentiation as between cheap , which means coming with a significantly reduced price and quality, and keen , considered as maintaining a certain amount of quality at a reduced total cost. For example, the 50 000 USD UBR1 is a low cost version 250 000 USD up to 400 000 USD PR2 of Willow Garage , but still is far away from the term cheap in a common way . Another example and at the same time another robot Melonee Wise worked on is the TurtleBot , which was constructed with the attempt to be the lowest cost version of a ROS robot at time of creation.1
How to achieve low cost?
There is no general solution to this problem. But an approach to solve the issue in the robotic context is to replace expensive single purpose solutions produced by companies in low quantities with mass produced products that get customized to suit the application.
A demonstration of this positive misuse are the first versions of the TurtleBot . Instead of constructing the robot with expensive 3D Laser Scanners they replaced it by a Microsoft Kinect originating from the gaming industry. Furthermore, it used a iRobot Roomba and later a iRobot Create as a low cost mobile base as constructing a custom movable footprint would
have been way more expensive. Also, the mass produced product came at a lower cost and unharmed warranty. An important side-effect of these replaceable parts is the independence of unique cost intensive and sometimes, due to customs regulations, not easily accessible parts. By that, the power to choose a cheap replacement at any time reduces overall expenses and
As a consequence, an low cost UGV should be easy to build and reproduce, affordable for education and able to run ROS with some kind of 3D measuring device. It further should consist of easily achievable or replaceable parts.
In conclusion, these properties lead to a modular design concept with communication inter- faces between the inexpensive components. Also a certain degree of flexibility is required to maintain extensibility and independence of expensive parts.
One of the most important packages a ROS robot should implement is TF (Transformations), because it enables the robot to keep track of multiple coordinate systems (frames) and their relations between each other over time. Following the ROS Enhancement Proposal s (REPs) especially REP105 the most global frame should be the world frame. Every other frame derives from it in a tree structure and can be transformed back into world coordinates by using the same units of measurement defined in REP103.
Another important frame tree is the robot itself. Starting with a mobile base_link further attached elements called links like wheels or cameras have their own frame and are connected via relations, also called joints. Those joints can be static or dynamic. A sample configuration can be seen in the following images:
ROS simple robot TF tree, illustrating a mobile base with four wheels and a xtion depth sensor
rviz visualization of the very same example robot
To define a robot, ROS offers a special XML description file using the Unified Robot Description Format (URDF) which is further improved by special markups and an additional interpreter called XML Macros (XACRO). In ROS, all not time-related relations can be defined in a single file and can be published periodically by the robot_state_publisher for example for simulation purposes. In advanced setups, publishing the robots joint states and especially the relation of the base_link is a complex task. Therefore it gets divided into separate processes like navigation, mapping or the hardware controllers.
I reactivated my old laptop and released the source code of the amosero robot. In case you want to build your own – do not hesitate to contact me to get a detailed construction info for free.
Have a nice day!