std.robotics — Kinematics & Autonomous Motion Control
Enterprise robotics engineering: Denavit-Hartenberg (DH) parameter forward kinematics, minimum-jerk quintic trajectory splines, and anti-windup PID joint controllers.
1. 6-DOF Robot Arm Forward Kinematics
Model serial manipulators and compute end-effector Cartesian poses $(X, Y, Z)$ using standard DH parameters:
import std.robotics.kinematics
import std.io
pub fn main() {
let mut arm = kinematics.create_robot_arm("UR10e-Enterprise")
// Add 6 standard rotational joints (theta, d, a, alpha)
kinematics.add_joint(&mut arm, 0.1625, 0.0, 1.5708, 0.0)
kinematics.add_joint(&mut arm, 0.0, -0.425, 0.0, -1.5708)
kinematics.add_joint(&mut arm, 0.0, -0.3922, 0.0, 0.0)
kinematics.add_joint(&mut arm, 0.1333, 0.0, 1.5708, 0.0)
kinematics.add_joint(&mut arm, 0.0997, 0.0, -1.5708, 0.0)
kinematics.add_joint(&mut arm, 0.0996, 0.0, 0.0, 0.0)
let pose = kinematics.compute_forward_kinematics(&arm)
std.io.println("End-Effector Pose: X=" + pose.x.to_string() +
" Y=" + pose.y.to_string() +
" Z=" + pose.z.to_string())
}
2. Minimum-Jerk Quintic Trajectory Planning
Generate smooth $C^2$-continuous trajectory splines with zero starting and ending jerk:
import std.robotics.kinematics
import std.io
pub fn main() {
let start_pos = 0.0
let target_pos = 1.5707963 // 90 degrees
let duration = 5.0 // seconds
// Evaluate waypoint at t = 2.5s (midpoint)
let pos_mid = kinematics.evaluate_quintic_trajectory(start_pos, target_pos, duration, 2.5)
std.io.println("Midpoint Joint Angle: " + pos_mid.to_string() + " rad") // 0.785398 rad
}
3. Cascaded PID Controller with Anti-Windup
import std.robotics.kinematics
import std.io
pub fn main() {
let mut pid = kinematics.create_pid(45.0, 0.5, 2.1, 100.0)
let target_pos = 1.5708
let current_pos = 0.0
let dt = 0.01
let torque = kinematics.compute_pid_torque(&mut pid, target_pos, current_pos, dt)
std.io.println("Calculated Motor Torque: " + torque.to_string() + " Nm")
}