kazu
9947dced15
added new helper methods/classes (e.g. for heading) new test cases optimize the dijkstra cleanups/refactoring added timed-benchmarks to the log many more...
142 lines
3.8 KiB
C++
142 lines
3.8 KiB
C++
#ifndef GRIDWALKWEIGHTED_H
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#define GRIDWALKWEIGHTED_H
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#include "../../geo/Heading.h"
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#include "../Grid.h"
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#include "../../math/DrawList.h"
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#include <KLib/math/distribution/Normal.h>
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/**
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* perform walks on the grid based on some sort of weighting
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* and drawing from the weighted elements
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*/
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template <typename T> class GridWalkWeighted {
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public:
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struct State {
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const T* node;
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Heading heading;
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State() : node(nullptr), heading(0) {;}
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State(const T* node, Heading heading) : node(node), heading(heading) {;}
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};
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private:
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/** per-edge: change heading with this sigma */
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static constexpr float HEADING_CHANGE_SIGMA = Angle::degToRad(3);
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/** per-edge: allowed heading difference */
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static constexpr float HEADING_DIFF_SIGMA = Angle::degToRad(30);
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/** allows drawing elements according to their probability */
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DrawList<T&> drawer;
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/** fast random-number-generator */
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std::minstd_rand gen;
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/** 0-mean normal distribution */
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std::normal_distribution<float> headingChangeDist = std::normal_distribution<float>(0.0, HEADING_CHANGE_SIGMA);
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public:
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template <int gridSize_cm> State getDestination(Grid<gridSize_cm, T>& grid, State start, float distance_m) {
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int retries = 2;
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State res;
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// try to walk the given distance from the start
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// if this fails (reached a dead end) -> restart (maybe the next try finds a better path)
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do {
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res = walk(grid, start, distance_m);
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} while (res.node == nullptr && --retries);
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// still reaching a dead end?
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// -> try a walk in the opposite direction instead
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if (res.node == nullptr) {
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res = walk(grid, State(start.node, start.heading.getInverted()), distance_m);
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}
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// still nothing found? -> keep the start as-is
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return (res.node == nullptr) ? (start) : (res);
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}
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private:
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static Heading getHeading(const T& from, const T& to) {
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return Heading(from.x_cm, from.y_cm, to.x_cm, to.y_cm);
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}
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template <int gridSize_cm> State walk(Grid<gridSize_cm, T>& grid, State cur, float distRest_m) {
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drawer.reset();;
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// calculate the weight for all possible destinations from "cur"
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for (T& neighbor : grid.neighbors(*cur.node)) {
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// heading when walking from cur to neighbor
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const Heading potentialHeading = getHeading(*cur.node, neighbor);
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// angular difference
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const float diff = cur.heading.getDiffHalfRAD(potentialHeading);
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// probability for this direction change?
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double prob = K::NormalDistribution::getProbability(0, HEADING_DIFF_SIGMA, diff);
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// perfer locations reaching the target
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const double shortening = cur.node->distToTarget - neighbor.distToTarget;
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if (shortening > 0) {prob *= 5;}
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drawer.add(neighbor, prob);
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}
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State next(nullptr, cur.heading);
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// pick a random destination
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T& nDir = drawer.get();
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const Heading hDir = getHeading(*cur.node, nDir);
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//next.heading += (cur.heading.getRAD() - hDir.getRAD()) * -0.5;
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next.heading = hDir;
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next.heading += headingChangeDist(gen);
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// compare two neighbors according to their implied heading change
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auto compp = [&] (const T& n1, const T& n2) {
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Heading h1 = getHeading(*cur.node, n1);
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Heading h2 = getHeading(*cur.node, n2);
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const float d1 = next.heading.getDiffHalfRAD(h1);
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const float d2 = next.heading.getDiffHalfRAD(h2);
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return d1 < d2;
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};
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// pick the neighbor best matching the new heading
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auto it = grid.neighbors(*cur.node);
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T& nn = *std::min_element(it.begin(), it.end(), compp);
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next.node = &nn;
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// // pervent dramatic heading changes. instead: try again
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// if (cur.heading.getDiffHalfRAD(getHeading(*cur.node, nn)) > Angle::degToRad(60)) {
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// return State(nullptr, 0);
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// }
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// get the distance up to this neighbor
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distRest_m -= next.node->getDistanceInMeter(*cur.node);
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// done?
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if (distRest_m <= 0) {return next;}
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// another round..
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return walk(grid, next, distRest_m);
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}
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};
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#endif // GRIDWALKWEIGHTED_H
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