On Wed, Jan 23, 2013 at 9:14 PM, John Griessen <
jo...@industromatic.com> wrote:
> On 01/23/2013 06:44 PM, Samuel Rose wrote:
>>
>> John, can you break down the workflow you hope to automate in more detail?
>> This will help me with research.
>
>
> The work flow I am envisioning, without implementation details about kinds
> of processors,
> but a few assumptions about costs is:
>
> Parts like printed circuit cards and components and finished enclosures are
> picked from
> loose piles and assembled.
Ok, I see. It might actually be useful to progressively develop this.
That is to say: testing the above scenario before introducing the more
complex components below.
If you are envisioning using ROS, you can also model it with
integration testing via
http://www.ros.org/wiki/UnitTesting and
rosunit (was rostest).
> The piles may be in bins for more blind
> suppliers to deal with,
> but the "assembly line" I am seeing is run with vision systems that are easy
> to train by human
> operators -- neural net based with no math of kinematics used in vision 3D
> mapping, just
> neural recognizers done in hardware like a "chain of identical neurons
> comparing an incoming
> video pattern with its reference pattern". The more view angles the
> operators program, the more
> can be recognized. Those can be sorted into bins for action and dealt with.
> Some misses will
> happen, but can be reset by random stirring or digging in the pile of parts
> to recognize.
> Once recognized, a part can be grabbed and moved, and then the process
> starts over, but at a different
> position. After a few moves, a part is where you want it and can be
> assembled. The chips
> that can do these recognitions resolve to a decision in a millisecond, so
> there is good
> opportunity to make some programmed use of those decisions for grabbing
> objects to move.
>
> It's just half baked, but that's what I'm thinking of.
>
I know that ROS has it's own visual libraries. If those are not
sufficient, there is also
http://simplecv.org/ python libs that work
really well for this kind of pattern recognition (usually no 3D
needed)
> A tributary flow is taking in extruded rough recycled plastic, chopping it
> off,
> handling the block into one of James's milling cubes, milling it into an
> enclosure,
> and sending it along to the above process, and the chips to the remelt and
> extrude
> process TBD. Also using neural vision systems as in
>
http://www.cognimem.com/_docs/Datasheet/DS_CM1K.pdf
>
From what I understand of
http://simplecv.org/ you may not even need
the advanced neural vision systems. Check it out. Also, it's probably
worth looking at the vision already in ROS if you are planning on
using that, since it probably can do some of the same processing that
SimpleCV can do. In the end, these kinds of approaches very likely
will give you exactly what you need. There are various tricks with 2D
computer vision for recognizing patterns, without necessarily needing
to emulate a neural net.
> John Griessen