Table 1 Quadrant-based coordinate and angle transformations.

From: Path-based evaluation of deep learning models for solving inverse kinematics in a revolute-prismatic robot

Quadrant

Transformation applied

1st Quadrant

d = 200–400 mm, in increments of 0.15 mm

θ = 0° to 90°, in increments of 0.15°

2nd Quadrant

Swap \(\:x\) and \(\:y\)

Multiply \(\:x\) by − 1

Add \(\:90^\circ\:\) to \(\:\theta\:\)

3rd Quadrant

Multiply both \(\:x\) and \(\:y\) by − 1

Add 18\(\:0^\circ\:\) to \(\:\theta\:\)

4th Quadrant

Swap \(\:x\) and \(\:y\)

Multiply \(\:y\:\)by − 1

Add \(\:270^\circ\:\) to \(\:\theta\:\)

Back to article page