I have read the documentation for the Roboforth environment from STrobotics and recognized that this a nice way for programming a robot. What I missed is a sophisticated software library with predefined motion primitives. For example, for picking up a object, for regrasping or for changing a tool.
In other programming languages like Python or C++, a library is a convenient way for programming repetitive tasks and for storing expert knowledge into machine-readable files. Also a library is good way for not-so-talented programmers to get access on higher-level-functions. In my opinion Forth is the perfect language for implementing such an API, but I didn't find information about it. Where should I search? Are there any examples out there?
I am author of RoboForth, and you make a good point. I have approached the problem of starting off new users with videos on YouTube; see How to... (playlist with 6 items, e.g "ST Robotics How-to number 1 - getting started") which is a playlist covering basics and indeed tool changing.
I never wrote any starter programs, because the physical positions (coordinates) would be different from one user to the next, however I think it can be done, and I will do it. Thanks for the heads up.
Related
I am newbie with drones. I would like to develop a program to manage a drone using opencv to fly indoor over a line.
I am searching a lot of languages but most all of them are GPS based. I saw there is an alternative which calls SLAM to detect the position using the sensors.
Well I have a line in the floor and a camera on my drone. I like mission planner but I am not quite sure if it is the best choice. I will be using Parrot AR, but I would like to use any drone.
So I would like to use mission planner but I am not sure if it is the best choice.
What would be the best SDK you would recommend me to use in order to manage the drone not using the GPS points but relative locations or SLAM?
Well, you have the Parrot API ,and a couple of wrappers in different languages. Node-AreDrone for nodeJs, PyArdrone for python, and there is a wrapper coded in C# which I have used AR.Drone. It has a good user interface which you can see the both cameras, record and replay the videos, control the drone by clicking on buttons, you can see the metrics and configuration of the drone and you have also a way to send commands in a queue. Because I love c# and the features I've mentioned you have already in a user interface, I prefer this. Most of them are quite the same as they use the Parrot API inside by sending udp messages. I couldn't try others, so, there are a lot, and anybody could tell me which one is the best. For mission planner I couldn't find a good solution for indoors. So, for anyone who is lost and do not know here to start as I was. I recommend to select the language you want and search for the corresponding wrapper. If you like c# as me, so AR.Drone is a good choice.
Also if you want to do something with OpenCV. Copterface is a good example. You could implement it in any language with OpenCV.
I recently watched great google talks speech about Cling - C++ language interpreter. But I wonder if anyone except people at CERN (where it is developed) are using Cling, and how good it is from non-collider-physics-scientist point of view, can you write desktop apps with it?
There are some videos of uses cases different from the High Energy Physics: http://www.youtube.com/results?search_query=cling+c%2B%2B (I think first couple are the relevant ones)
It has the potential to be very useful, but it is very young. There is no documentation that I could find, no dedicated mailing list, no online tutorials. I was able to get small toy code to run, but couldn't figure out how to use it productively on a large library yet.
Cling project is well established one. You can find more information in their official website cling. They also have a forum
Thanks
I am basically just starting out in computer programming; mostly fluent in basic Java. I have an idea of creating an ASL (American Sign Language) to English, and my initial problem is how to identify hand movement from a webcam then comparing them to Signs that is already stored as an image or another video. If the problem is a bit too advanced for me then please list any major concepts that I can learn. Please and thank you.
You clearly have a challenging problem ^^. Try to explain all you need to solve your problem would be very hard, mainly because there many ways to do this. I advice you to read a nice book about image processing (Gonzalez' book is a nice choice) and the OpenCV documentation (but it is implemented in C, C++ and has Python bindings; although it's a library that implements a lot of image processing techniques). Maybe you should focus your study on feature detection, motion analysis and object tracking. As sign language uses not just hand sign (static state) but also hand moviments (dynamic state) to express something, object tracking may be a good way to describe the signs.I hope these informations help you, at least a little -^.^- Bye bye.
Look at OpenCV. They have a lot of libraries that you might find handy.
http://opencv.willowgarage.com/wiki/
I am searching for an algorithm to determine whether realtime audio input matches one of 144 given (and comfortably distinct) phoneme-pairs.
Preferably the lowest level that does the job.
I'm developing radical / experimental musical training software for iPhone / iPad.
My musical system comprises 12 consonant phonemes and 12 vowel phonemes, demonstrated here. That makes 144 possible phoneme pairs. The student has to sing the correct phoneme pair 'laa duu bee' etc in response to visual stimulus.
I have done a lot of research into this, it looks like my best bet may be to use one of the iOS Sphinx wrappers ( iPhone App › Add voice recognition? is the best source of information I have found ). However, I can't see how I would adapt such a package, can anyone with experience using one of these technologies give a basic rundown of the steps that would be required?
Would training be necessary by the user? I would have thought not, as it is such an elementary task, compared with full language models of thousands of words and far greater and more subtle phoneme base. However, it would be acceptable (not ideal) to have the user train 12 phoneme pairs: { consonant1+vowel1, consonant2+vowel2, ..., consonant12+vowel12 }. The full 144 would be too burdensome.
Is there a simpler approach? I feel like using a fully featured continuous speech recogniser is using a sledgehammer to crack a nut. It would be far more elegant to use the minimum technology that would solve the problem.
So really I'm hunting for any open source software that recognises phonemes.
PS I need a solution which runs pretty much real-time. so even as they are singing the note, firstly it blinks on to illustrate that it picked up the phoneme pair that was sung, and then it glows to illustrate whether they are singing the correct note pitch
If you are looking for a phone-level open source recogniser, then I would recommend HTK. Very good documentation is available with this tool in the form of the HTK Book. It also contains an entire chapter dedicated to building a phone level real-time speech recogniser. From your problem statement above, it seems to me like you might be able to re-work that example into your own solution. Possible pitfalls:
Since you want to do a phone level recogniser, the data needed to train the phone models would be very high. Also, your training database should be balanced in terms of distribution of the phones.
Building a speaker-independent system would require data from more than one speaker. And lots of that too.
Since this is open-source, you should also check into the licensing info for any additional details about shipping the code. A good alternative would be to use the on-phone recorder and then have the recorded waveform sent over a data channel to a server for the recognition, pretty much something like what google does.
I have a little bit of experience with this type of signal processing, and I would say that this is probably not the type of finite question that can be answered definitively.
One thing worth noting is that although you may restrict the phonemes you are interested in, the possibility space remains the same (i.e. infinite-ish). User training might help the algorithms along a bit, but useful training takes quite a bit of time and it seems you are averse to too much of that.
Using Sphinx is probably a great start on this problem. I haven't gotten very far in the library myself, but my guess is that you'll be working with its source code yourself to get exactly what you want. (Hooray for open source!)
...using a sledgehammer to crack a nut.
I wouldn't label your problem a nut, I'd say it's more like a beast. It may be a different beast than natural language speech recognition, but it is still a beast.
All the best with your problem solving.
Not sure if this would help: check out OpenEars' LanguageModelGenerator. OpenEars uses Sphinx and other libraries.
http://www.hfink.eu/matchbox
This page links to both YouTube video demo and github source.
I'm guessing it would still be a lot of work to mould it into the shape I'm after, but is also definitely does do a lot of the work.
I have been working with Carmen http://carmen.sourceforge.net/ for a while now, and I really like the software but I need to make some changes inside the source code.
I am therefore interesting in some students reports/projects there have been working with Carmen, or any documentation of the source code.
I have been reading the documentation on the webpage for Carmen, but with all respect I think the literature there is a bit outdated and insufficient.
ROS is the new hot navigation toolkit for robotics. It has a professional development group and a very active community. The documentation is okay, but it's the best I've seen for robotic operating systems.
There are a lot of student project teams that are using it.
Check it out at www.ros.org
I'll be more specific on why ROS is awesome...
Built in visualizer/simulator rviz
- It has a record function which will record all of the messages passed out of nodes, this allows you take in a lot of raw data store it in a "ros bag" and then play it back later when you need to test your AI, but want to sit in your bed.
Built in navigation capabilities,
-all you have to do is write the publishers of data for your sensors.
-It has standard messages that you need to fill out so that the stack has enough information.
There is an Extended Kalman Filter which is pretty awesome because I didn't want to write one. Currently implementing it, i'll let you know how that turns out.
It also has built in message levels, by that I mean you can change which severity of print messages are printed during runtime, fairly handy for debugging.
There's a robot monitor node that you can publish the status of your sensors to and it bundles all of that information into a GUI for your viewing pleasure.
There are some basic drivers already written. For example SICK lidars are supported right out of the box.
There is also a built in transform function, to help you move everything to the right coordinate system.
ROS was made to run across multiple computers, but can work on just one.
Data transfer is handled over TCP ports.
I hope that's more helpful.