Obstacle avoidance with an UGV -HELP PLEASE-

rgallant:
It does all seem to be flight based that's for sure. But can you define your parameters a bit better, when you say navigate via gps do you mean move from known starting point to a known waypoint ? Or from a known start point to a waypoint that is identified by a system other than gps coordinates ?

Sorry--didn't see your post!

what do you mean flight based? And navigating for starters will consist of the Arduino knowing its current position and the coordinates of the goal it want to get to. Later programming might involve other waypoints, but right now I'm sticking with going from point "a" (current position) to "b" (another, known position--but not the current position)

Also a next revision is probably going to include GPS differential, but that's a bit out of my reach currently

Sorry that was badly worded most of the examples are for some form of aircraft. But that link I added should give you a start on navigating from waypoint to waypoint.

I cannot tell you how much this is really going to help me!! The GPS library is the same one I use, but for a different GPS, and I have the same digital compass! Thanks a bunch!! But back to the obstacle avoidance---any advice/ ideas to get me going?

I have some ideas, but currently I have just started playing with ping sensors ,the mail man was good to me today.

I will play around a bit and see what I can come up, I want to build an autonomous tank so it fits in.

The big issue I see with the small servo movements, is sort of a constant left/right turn effect if the the range and time is too small.

If your current code gets you down halls pretty centered then perhaps the problem is more defining a minimum distance. So if we are with in X distance on the left side adjust to steer right a bit and move forward. You have a forward arc set up it should be possible to tweak the settings to curve away from or around obstacles. Which is what you want to accomplish in general terms.

Let me put some thought into this, I need to scrawl some stuff on paper and scan it so it makes sense. I can see in my mind what you want to do, but it is the end of a long work day and I keep coming up the gibberish.

Do a search on "robomagellan" for background material on GPS and
outdoors travel.

So the whole reason I started this project was for a program in my high school called the Authentic Science Research Program. I'm not sure if you've heard of it or not--we apply to the Intel talent search senior year. I'm currently a junior. Currently, I want to do a study on something in the robotics category, but it looks like I'm going to study obstacle avoidance. Does anyone have any ideas for a research topic for me? I need some sort of "question" that hasn't been answered or something that has never been done before. Maybe create a new obstacle avoidance method, I'm not sure---but any ideas?

For algorithms, consider using a Braitenberg "algorithm" for you avoidance mechanism. This works well as a "background" always on behaviour.

Also consider Potential fields - might work better with your multiple sonar setup:

http://www.cs.mcgill.ca/~hsafad/robotics/index.html

I've seen the potential field method before, and it seems to have its various drawbacks. Maybe improving one of these methods might make a good research topic.......also, would I have to use interrupts to make the GPS tell the car to go from point "a" to "b" and have the car avoid obstacles along the way?

also, would I have to use interrupts to make the GPS tell the car to go from point "a" to "b" and have the car avoid obstacles along the way?

Do you understand what interrupts are? A ringing phone is an interrupt. It occurs at random times, needs pretty much immediate attention, and is externally caused. Which of those attributes applies to you (not the the GPS) telling the car to go from point a to point b? How will you define point b? All the the GPS does is tell the car where it currently is. It still needs to figure out where point b is in relationship to where it is now, and make the appropriate turn(s) to move in the direction of point b, until "here" is point b.

For background on pathfinding, look up the A* Algorithm.

something that has never been done before.

Good luck. Do you know how many grad students there have been at MIT and CMU
trying everything and its cousins in regards robotics. Zillions upon zillions.

OTOH, to me, to a large extent robotics bogged down in the 90s, when a lot of people
started using subsumption and really went nowheres. AI-robotics kind of stalled, although
there have been some "minor" advances in the 2000s. For many years, people in robotics
clubs did little more than build line-followers and mini-sumos. At least robot soccer and
robomagellan were a huge step up.

If you're interested in following up robotics in your future education, learn AI and apply it
to robotics. There are too many dumb robots around.

PaulS:
Do you understand what interrupts are? A ringing phone is an interrupt. It occurs at random times, needs pretty much immediate attention, and is externally caused. Which of those attributes applies to you (not the the GPS) telling the car to go from point a to point b? How will you define point b? All the the GPS does is tell the car where it currently is. It still needs to figure out where point b is in relationship to where it is now, and make the appropriate turn(s) to move in the direction of point b, until "here" is point b.

Yes, I understand interrupts and I created a program which was originally going to use interrupts for the car, but stopped because I needed the algorithm and GPS code first. I thought I would have to use interrupts to constantly check the IR sensors, and when they detect something within a certain range, avoid the obstacle, then get back on course. Point a is the starting location and point b is the goal location. I didn't mean use interrupts in the GPS literally---maybe my wording is just bad.......but I thought in order to read from sensors and go to a place, I would need interrupts. Does this still hold true?

oric_dan(333):
If you're interested in following up robotics in your future education, learn AI and apply it
to robotics. There are too many dumb robots around.

I'm sorry but just for clarification AI is artificial intelligence right? Just want to make sure---and what exactly would it do for robots? I'm not fully understanding what you mean.... :-/

but I thought in order to read from sensors and go to a place, I would need interrupts. Does this still hold true?

I don't think so. If the car/robot/UGV is moving at 60 mph, you might. At more reasonable speeds, polling is usually good enough.

So just run through a loop of checking GPS data, updating servo, check IR sensors(in a while loop), and if there is a "problem" (ie--obstacle in the way) avoid the obstacle, then go back to the gps?

I'm not fully understanding what you mean

AI is about intelligence. Most robots are not very intelligent.

oric_dan(333):

I'm not fully understanding what you mean

AI is about intelligence. Most robots are not very intelligent.

Actually, AI today seems to be more focused on statistics and probabilities than anything else.

For the OP: If you are serious about this project, you'll want to take a few online (but free) classes. They will teach you a lot; also - if your knowledge/understanding of probabilities, statistics, and linear algebra is lacking, you'll want to brush up on those, first - ask me how I know...

Anyhow - here are the classes:

Free Intro Statistics Course | Free Courses | Udacity - Intro to Stats
Introduction to Artificial Intelligence | Free Courses | Udacity - Intro to AI
Artificial Intelligence for Robotics | Free Courses | Udacity - AI for Robotics
https://www.coursera.org/course/ml - Machine Learning

You might also check out some of the other offerings; there are many other classes which could be beneficial from a robotics and AI/ML standpoint.

The "CS271 - Intro to AI" is a recent addition to Udacity's lineup; it was only recently announced. It, along with the Machine Learning course from Coursera, were the two original "free online" courses brought out by Stanford last fall/winter of 2011. I took both, but I ended up having to drop out of the AI class about halfway through due to personal issues related to my employment. I went on to complete the ML class, then earlier this year (spring 2012) I took the "CS373 - AI for Robotics" class from Udacity (Coursera and Udacity both were spinoffs that grew out of those original two Stanford classes; this was done so as not to dilute the Stanford brand and recognition, while being able to bring more courses and such to a wider audience).

I found the CS373 class to be very challenging - you basically learn enough in it to "build" (well, program) a simplified version of a self-driving, path-planning robotic vehicle, much like Google's self-driving car (take a guess as to who Prof Sebastian Thrun is, and what his relationship is to robotics and Google's car)...

The "Intro to AI" and "Machine Learning" courses are more generalized; geared toward a broad overview and application of AI/ML algorithms to computing problems in general - but I found them very interesting, and one guy in the ML course created this based on his learning and study:

http://blog.davidsingleton.org/nnrccar

...so you can see that all of these courses have application and knowledge which can transfer to robotics, path-planning, navigation, etc.

I'm personally glad that Udacity brought back the "Intro to AI" course; many people were wanting it back, and when I had to drop out, it was during a very difficult time for me, and I hated having to do it. I intend to go back and take the course again, as I found it very instructive and informative (and it wasn't easy, either - I'm also going to take that intro to stats course as well - if there is one thing these courses have taught me, it's that I am very lacking in my understanding of statistics and probabilities, and understanding of both are needed for modern AI/ML algorithm implementations).

Oh - one other thing - that CS373 course offers one of the best explanations of the PID algorithm I have ever seen; you go from zero understanding of PID, to implementing the P, then the D, then the I (they do it this way to prove a point about the algorithm) - and having a real solid understanding of it at the end. The coding was done in Python (whereas the coding for the ML class was done in Octave/MatLab - which is a very cool language all on its own, but takes a bit to wrap your head around the primitives of vectors and matrices - but once you understand how to vectorize an algorithm, the possibility of immense processing power understanding is unleashed; if only I had access to a Beowulf cluster, or a nVidia Tesla machine!)...

]Might be a bit much for one sitting.
OP is 16-YO.

oric_dan(333):
]Might be a bit much for one sitting.
OP is 16-YO.

Perhaps, but that CS373 course is excellent. Even if the OP doesn't take it all in on the first sitting, it's worth doing because it'll be an eye opener on just how hard the problem domain is.

I looked at the course with Sebastian Thrun recently, and I'll look into them in the future. So if I understand, A robot has artificial intelligence if bases its movements on probability, as well as the real world interface and sensing? And I would also assume that it is programmed to react to every possible scenario that could ever happen--am I correct?

So if I understand, A robot has artificial intelligence if bases its movements on probability, as well as the real world interface and sensing? And I would also assume that it is programmed to react to every possible scenario that could ever happen--am I correct?

Well, this is a little optimistic. Thrun wrote a book called Probabilistic Robotics, and his
robot driving course material comes straight out of this book [except for the Python
programming stuff]. He believes the best way to interpret sensor data is probabilistically.

Also, one of the prime areas of AI research is currently in Bayesian Networks, which are
also a probabilistic fabrication. However, there are many many many other methodologies
at use in AI, which would be covered in an introductory course.

Also, the idea of "react to every possible scenario" is a deeply philosophical question, that
has been discussed for decades. Practically speaking, most AI systems function in a
relatively limited domain, and would fail immediately if loosed upon the world in general.
Even humans cannot deal with every possible scenario very well, so it might be a while
before AI systems and robots are able to.