Is LinuxCNC/machinekit  the right tool for this?   It's advantage is that
it has a fast servo loop but there are many things in does not do and
linuxcnc is basically a g-code interpreter and unless the robot arm is just
doing blind repetitive work g-code is not going to work.  For example I
don't see how to integrate g-code with a vision system.

Why step back and describe the big picture?  What is the goal?   And what
kind of motors do you have.  Are these servo motors with encoders?  You are
asking very low level detailed questions but with no context.

See the link below.  This is what I'm using to control an arm.  The article
is very high level and assumes a lot about the reader.  But in general arm
control is not easy so you REALLY want to make use of existing software.
This describes how to use some existing software for "constraint controlled
inverse kinematics" of 6+ DOF arms.  It can compute all the motion planning
and accelerations and torques to (say) reach around an obstacle and place
the end effector at the desired location and orientation all in realtime.

I would strongly suggest getting this all to work on standard PC hardware.
One that has good performance and loads of RAM.   Then if needed move the
solution to a smaller platform like RPi3.    Starting on the small platform
is not nearly as easy.

....ros.org/arm_navigation/Tutorials/Running arm navigation...
<http://wiki.ros.org/arm_navigation/Tutorials/Running%20arm%20navigation%20on%20non-PR2%20arm>




On Sat, Sep 30, 2017 at 9:29 AM, thangle <[email protected]> wrote:

> Hi,
>
> i having a project about control arm robot, and i'm research dynamic
> control ( computed torque control PD). If any projects used to do it in
> linuxcnc(machinekit? One thing i can do is write a C code to calculate
> Torque of joints. I dont know how to intergrate them into linuxcnc. It's
> great if there are some examples for reference because i'm still quite new
> in linuxcnc(machinekit) and embedded.
>
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-- 

Chris Albertson
Redondo Beach, California

-- 
website: http://www.machinekit.io blog: http://blog.machinekit.io github: 
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