Closed leemengwei closed 4 years ago
Hello @leemengwei,
could you paste the code that procuded these outputs?
Ops the codes are gone, I've modified them, yet my uncertain remains the same. The first matrix I gave out
[[[ 1. 0. 0. 0. ]
[ 0. 1. 0. 0. ]
[ 0. 0. 1. 0. ]
[ 0. 0. 0. 1. ]]
is something like base frame, world frame or say, I guess.
The second matrix
[[ 1. 0. 0. 0. ]
[ 0. -0. -1. -0.43]
[ 0. 1. -0. 0. ]
[ 0. 0. 0. 1. ]]
is frame I got after forward kinematic , something I got by:
new_frame = np.array(my_chain.forward_kinematics(six_axis)
what does the second matrix mean? Is't transformation from base to new, or from new to base? (I think is the new to base, am I right?) Thanks
This matrix is a position/orientation of the end-effector encoded as homogeneous coordinates;
[[ 1. 0. 0. 0. ]
[ 0. -0. -1. -0.43]
[ 0. 1. -0. 0. ]
[ 0. 0. 0. 1. ]]
With this, you can see that your position is matrix[3, :3]
, so here the position of your end-effector is [0, -0.43, 0]
Hi, thanks for your work. Great one, I like it. I'm doing some deeplearning research, thus I'm not quite familiar with robotics. Sorry for my innocent, So here's my problem about what is 'real_frame' in code, does that mean to transform from end-effector to old one? (I think it is, but not sure) Take two frame for example, world_frame and new_frame, when joint is given:
then returned two frames are: [[[ 1. 0. 0. 0. ] [ 0. 1. 0. 0. ] [ 0. 0. 1. 0. ] [ 0. 0. 0. 1. ]]
[[ 1. 0. 0. 0. ] [ 0. -0. -1. -0.43] [ 0. 1. -0. 0. ] [ 0. 0. 0. 1. ]]