Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks

Table 2

Multimodal orbital angular momentum algorithm steps.

Algorithm step number	Multimodal orbital angular content
In the complex configuration	Static int interspacef = brick_width;
For the robot designer	Node_pos = [(1, 0), (0, 1), (2, 1), (1, 2)]
The d-h algorithm provides	Bbox_args = dict(boxstyle = “round”)
A convenient method	Head and tail patch, respectively.
In the configuration of the	Xycoords: str, artist, transform
Posture of adjacent joint	Import matplotlib.pyplot as plt
In order to find the position	Import numpy as np
For finding positive kinematics	From matplotlib.lines import line2d
to be solved	Arrowprops = dict(patcha = an1)
of the robot for the design h	Patchb = an2, matplotlib.patch.
is one of the typical problems	Connectionstyle = “arc3,rad = 0.2″
Joint configuration and kinematics	#color = “red”, patch instance