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MarlinFirmware/Marlin/planner_bezier.cpp
Scott Lahteine 77639672d7 Move NONLINEAR bed leveling to planner
This is in advance of moving non-linear bed leveling to the planner
class.
2016-09-18 13:50:38 -05:00

201 lines
7.9 KiB
C++

/**
* Marlin 3D Printer Firmware
* Copyright (C) 2016 MarlinFirmware [https://github.com/MarlinFirmware/Marlin]
*
* Based on Sprinter and grbl.
* Copyright (C) 2011 Camiel Gubbels / Erik van der Zalm
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*
*/
/**
* planner_bezier.cpp
*
* Compute and buffer movement commands for bezier curves
*
*/
#include "Marlin.h"
#if ENABLED(BEZIER_CURVE_SUPPORT)
#include "planner.h"
#include "language.h"
#include "temperature.h"
// See the meaning in the documentation of cubic_b_spline().
#define MIN_STEP 0.002
#define MAX_STEP 0.1
#define SIGMA 0.1
/* Compute the linear interpolation between to real numbers.
*/
inline static float interp(float a, float b, float t) { return (1.0 - t) * a + t * b; }
/**
* Compute a Bézier curve using the De Casteljau's algorithm (see
* https://en.wikipedia.org/wiki/De_Casteljau%27s_algorithm), which is
* easy to code and has good numerical stability (very important,
* since Arudino works with limited precision real numbers).
*/
inline static float eval_bezier(float a, float b, float c, float d, float t) {
float iab = interp(a, b, t);
float ibc = interp(b, c, t);
float icd = interp(c, d, t);
float iabc = interp(iab, ibc, t);
float ibcd = interp(ibc, icd, t);
float iabcd = interp(iabc, ibcd, t);
return iabcd;
}
/**
* We approximate Euclidean distance with the sum of the coordinates
* offset (so-called "norm 1"), which is quicker to compute.
*/
inline static float dist1(float x1, float y1, float x2, float y2) { return fabs(x1 - x2) + fabs(y1 - y2); }
/**
* The algorithm for computing the step is loosely based on the one in Kig
* (See https://sources.debian.net/src/kig/4:15.08.3-1/misc/kigpainter.cpp/#L759)
* However, we do not use the stack.
*
* The algorithm goes as it follows: the parameters t runs from 0.0 to
* 1.0 describing the curve, which is evaluated by eval_bezier(). At
* each iteration we have to choose a step, i.e., the increment of the
* t variable. By default the step of the previous iteration is taken,
* and then it is enlarged or reduced depending on how straight the
* curve locally is. The step is always clamped between MIN_STEP/2 and
* 2*MAX_STEP. MAX_STEP is taken at the first iteration.
*
* For some t, the step value is considered acceptable if the curve in
* the interval [t, t+step] is sufficiently straight, i.e.,
* sufficiently close to linear interpolation. In practice the
* following test is performed: the distance between eval_bezier(...,
* t+step/2) is evaluated and compared with 0.5*(eval_bezier(...,
* t)+eval_bezier(..., t+step)). If it is smaller than SIGMA, then the
* step value is considered acceptable, otherwise it is not. The code
* seeks to find the larger step value which is considered acceptable.
*
* At every iteration the recorded step value is considered and then
* iteratively halved until it becomes acceptable. If it was already
* acceptable in the beginning (i.e., no halving were done), then
* maybe it was necessary to enlarge it; then it is iteratively
* doubled while it remains acceptable. The last acceptable value
* found is taken, provided that it is between MIN_STEP and MAX_STEP
* and does not bring t over 1.0.
*
* Caveat: this algorithm is not perfect, since it can happen that a
* step is considered acceptable even when the curve is not linear at
* all in the interval [t, t+step] (but its mid point coincides "by
* chance" with the midpoint according to the parametrization). This
* kind of glitches can be eliminated with proper first derivative
* estimates; however, given the improbability of such configurations,
* the mitigation offered by MIN_STEP and the small computational
* power available on Arduino, I think it is not wise to implement it.
*/
void cubic_b_spline(const float position[NUM_AXIS], const float target[NUM_AXIS], const float offset[4], float fr_mm_s, uint8_t extruder) {
// Absolute first and second control points are recovered.
float first0 = position[X_AXIS] + offset[0];
float first1 = position[Y_AXIS] + offset[1];
float second0 = target[X_AXIS] + offset[2];
float second1 = target[Y_AXIS] + offset[3];
float t = 0.0;
float bez_target[4];
bez_target[X_AXIS] = position[X_AXIS];
bez_target[Y_AXIS] = position[Y_AXIS];
float step = MAX_STEP;
millis_t next_idle_ms = millis() + 200UL;
while (t < 1.0) {
thermalManager.manage_heater();
millis_t now = millis();
if (ELAPSED(now, next_idle_ms)) {
next_idle_ms = now + 200UL;
idle();
}
// First try to reduce the step in order to make it sufficiently
// close to a linear interpolation.
bool did_reduce = false;
float new_t = t + step;
NOMORE(new_t, 1.0);
float new_pos0 = eval_bezier(position[X_AXIS], first0, second0, target[X_AXIS], new_t);
float new_pos1 = eval_bezier(position[Y_AXIS], first1, second1, target[Y_AXIS], new_t);
for (;;) {
if (new_t - t < (MIN_STEP)) break;
float candidate_t = 0.5 * (t + new_t);
float candidate_pos0 = eval_bezier(position[X_AXIS], first0, second0, target[X_AXIS], candidate_t);
float candidate_pos1 = eval_bezier(position[Y_AXIS], first1, second1, target[Y_AXIS], candidate_t);
float interp_pos0 = 0.5 * (bez_target[X_AXIS] + new_pos0);
float interp_pos1 = 0.5 * (bez_target[Y_AXIS] + new_pos1);
if (dist1(candidate_pos0, candidate_pos1, interp_pos0, interp_pos1) <= (SIGMA)) break;
new_t = candidate_t;
new_pos0 = candidate_pos0;
new_pos1 = candidate_pos1;
did_reduce = true;
}
// If we did not reduce the step, maybe we should enlarge it.
if (!did_reduce) for (;;) {
if (new_t - t > MAX_STEP) break;
float candidate_t = t + 2.0 * (new_t - t);
if (candidate_t >= 1.0) break;
float candidate_pos0 = eval_bezier(position[X_AXIS], first0, second0, target[X_AXIS], candidate_t);
float candidate_pos1 = eval_bezier(position[Y_AXIS], first1, second1, target[Y_AXIS], candidate_t);
float interp_pos0 = 0.5 * (bez_target[X_AXIS] + candidate_pos0);
float interp_pos1 = 0.5 * (bez_target[Y_AXIS] + candidate_pos1);
if (dist1(new_pos0, new_pos1, interp_pos0, interp_pos1) > (SIGMA)) break;
new_t = candidate_t;
new_pos0 = candidate_pos0;
new_pos1 = candidate_pos1;
}
// Check some postcondition; they are disabled in the actual
// Marlin build, but if you test the same code on a computer you
// may want to check they are respect.
/*
assert(new_t <= 1.0);
if (new_t < 1.0) {
assert(new_t - t >= (MIN_STEP) / 2.0);
assert(new_t - t <= (MAX_STEP) * 2.0);
}
*/
step = new_t - t;
t = new_t;
// Compute and send new position
bez_target[X_AXIS] = new_pos0;
bez_target[Y_AXIS] = new_pos1;
// FIXME. The following two are wrong, since the parameter t is
// not linear in the distance.
bez_target[Z_AXIS] = interp(position[Z_AXIS], target[Z_AXIS], t);
bez_target[E_AXIS] = interp(position[E_AXIS], target[E_AXIS], t);
clamp_to_software_endstops(bez_target);
#if IS_KINEMATIC
inverse_kinematics(bez_target);
planner.buffer_line(delta[A_AXIS], delta[B_AXIS], delta[C_AXIS], bez_target[E_AXIS], fr_mm_s, extruder);
#else
planner.buffer_line(bez_target[X_AXIS], bez_target[Y_AXIS], bez_target[Z_AXIS], bez_target[E_AXIS], fr_mm_s, extruder);
#endif
}
}
#endif // BEZIER_CURVE_SUPPORT