CHolonomicFullEval.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
 
#include "nav-precomp.h"  // Precomp header
 
#include <mrpt/nav/holonomic/CHolonomicFullEval.h>
#include <mrpt/serialization/CArchive.h>
#include <mrpt/core/round.h>
#include <mrpt/math/geometry.h>
#include <mrpt/math/ops_containers.h>
#include <mrpt/serialization/stl_serialization.h>
#include <cmath>
 
using namespace mrpt;
using namespace mrpt::math;
using namespace mrpt::nav;
using namespace std;
 
IMPLEMENTS_SERIALIZABLE(
        CLogFileRecord_FullEval, CHolonomicLogFileRecord, mrpt::nav)
IMPLEMENTS_SERIALIZABLE(
        CHolonomicFullEval, CAbstractHolonomicReactiveMethod, mrpt::nav)
 
const unsigned int INVALID_K = std::numeric_limits<unsigned int>::max();
 
CHolonomicFullEval::CHolonomicFullEval(
        const mrpt::config::CConfigFileBase* INI_FILE)
        : CAbstractHolonomicReactiveMethod("CHolonomicFullEval"),
          m_last_selected_sector(std::numeric_limits<unsigned int>::max())
{
        if (INI_FILE != nullptr) initialize(*INI_FILE);
}
 
void CHolonomicFullEval::saveConfigFile(mrpt::config::CConfigFileBase& c) const
{
        options.saveToConfigFile(c, getConfigFileSectionName());
}
 
void CHolonomicFullEval::initialize(const mrpt::config::CConfigFileBase& c)
{
        options.loadFromConfigFile(c, getConfigFileSectionName());
}
 
struct TGap
{
        int k_from, k_to;
        double min_eval, max_eval;
        bool contains_target_k;
        /** Direction with the best evaluation inside the gap. */
        int k_best_eval;
 
        TGap()
                : k_from(-1),
                  k_to(-1),
                  min_eval(std::numeric_limits<double>::max()),
                  max_eval(-std::numeric_limits<double>::max()),
                  contains_target_k(false),
                  k_best_eval(-1)
        {
        }
};
 
CHolonomicFullEval::EvalOutput::EvalOutput() : best_k(INVALID_K), best_eval(.0)
{
}
 
void CHolonomicFullEval::evalSingleTarget(
        unsigned int target_idx, const NavInput& ni, EvalOutput& eo)
{
        ASSERT_(target_idx < ni.targets.size());
        const auto target = ni.targets[target_idx];
 
        using mrpt::square;
 
        eo = EvalOutput();
 
        const auto ptg = getAssociatedPTG();
        const double ptg_ref_dist = ptg ? ptg->getRefDistance() : 1.0;
        const size_t nDirs = ni.obstacles.size();
 
        const double target_dir = ::atan2(target.y, target.x);
        const unsigned int target_k =
                CParameterizedTrajectoryGenerator::alpha2index(target_dir, nDirs);
        const double target_dist = target.norm();
 
        m_dirs_scores.resize(nDirs, options.factorWeights.size() + 2);
 
        // TP-Obstacles in 2D:
        std::vector<mrpt::math::TPoint2D> obstacles_2d(nDirs);
 
        mrpt::obs::T2DScanProperties sp;
        sp.aperture = 2.0 * M_PI;
        sp.nRays = nDirs;
        sp.rightToLeft = true;
        const auto& sc_lut = m_sincos_lut.getSinCosForScan(sp);
 
        for (unsigned int i = 0; i < nDirs; i++)
        {
                obstacles_2d[i].x = ni.obstacles[i] * sc_lut.ccos[i];
                obstacles_2d[i].y = ni.obstacles[i] * sc_lut.csin[i];
        }
 
        const int NUM_FACTORS = 5;
 
        ASSERT_(options.factorWeights.size() == NUM_FACTORS);
 
        for (unsigned int i = 0; i < nDirs; i++)
        {
                double scores[NUM_FACTORS];  // scores for each criterion
 
                if (ni.obstacles[i] < options.TOO_CLOSE_OBSTACLE &&
                        !(i == target_k &&
                          ni.obstacles[i] > 1.02 * target_dist))  // Too close to obstacles?
                // (unless target is in
                // between obstacles and
                // the robot)
                {
                        for (int l = 0; l < NUM_FACTORS; l++) m_dirs_scores(i, l) = .0;
                        continue;
                }
 
                const double d = std::min(ni.obstacles[i], 0.95 * target_dist);
 
                // The TP-Space representative coordinates for this direction:
                const double x = d * sc_lut.ccos[i];
                const double y = d * sc_lut.csin[i];
 
                // Factor #1: collision-free distance
                // -----------------------------------------------------
                if (mrpt::abs_diff(i, target_k) <= 1 &&
                        target_dist < 1.0 - options.TOO_CLOSE_OBSTACLE &&
                        ni.obstacles[i] > 1.05 * target_dist)
                {
                        // Don't count obstacles ahead of the target.
                        scores[0] =
                                std::max(target_dist, ni.obstacles[i]) / (target_dist * 1.05);
                }
                else
                {
                        scores[0] =
                                std::max(0.0, ni.obstacles[i] - options.TOO_CLOSE_OBSTACLE);
                }
 
                // Discount "circular loop aparent free distance" here, but don't count
                // it for clearance, since those are not real obstacle points.
                if (ptg != nullptr)
                {
                        const double max_real_freespace =
                                ptg->getActualUnloopedPathLength(i);
                        const double max_real_freespace_norm =
                                max_real_freespace / ptg->getRefDistance();
 
                        mrpt::keep_min(scores[0], max_real_freespace_norm);
                }
 
                // Factor #2: Closest approach to target along straight line (Euclidean)
                // -------------------------------------------
                mrpt::math::TSegment2D sg;
                sg.point1.x = 0;
                sg.point1.y = 0;
                sg.point2.x = x;
                sg.point2.y = y;
 
                // Range of attainable values: 0=passes thru target. 2=opposite
                // direction
                double min_dist_target_along_path = sg.distance(target);
 
                // Idea: if this segment is taking us *away* from target, don't make the
                // segment to start at (0,0), since all
                // paths "running away" will then have identical minimum distances to
                // target. Use the middle of the segment instead:
                const double endpt_dist_to_target = (target - TPoint2D(x, y)).norm();
                const double endpt_dist_to_target_norm =
                        std::min(1.0, endpt_dist_to_target);
 
                if ((endpt_dist_to_target_norm > target_dist &&
                         endpt_dist_to_target_norm >= 0.95 * target_dist) &&
                        min_dist_target_along_path >
                                1.05 * std::min(
                                                   target_dist,
                                                   endpt_dist_to_target_norm)  // the path does not get
                        // any closer to trg
                        //|| (ni.obstacles[i]<0.95*target_dist)
                        )
                {
                        // path takes us away or way blocked:
                        sg.point1.x = x * 0.5;
                        sg.point1.y = y * 0.5;
                        min_dist_target_along_path = sg.distance(target);
                }
 
                scores[1] = 1.0 / (1.0 + square(min_dist_target_along_path));
 
                // Factor #3: Distance of end collision-free point to target (Euclidean)
                // -----------------------------------------------------
                {
                        scores[2] = std::sqrt(
                                1.01 - endpt_dist_to_target_norm);  // the 1.01 instead of 1.0
                        // is to be 100% sure we
                        // don't get a domain error
                        // in sqrt()
                }
 
                // Factor #4: Stabilizing factor (hysteresis) to avoid quick switch
                // among very similar paths:
                // ------------------------------------------------------------------------------------------
                if (m_last_selected_sector != std::numeric_limits<unsigned int>::max())
                {
                        const unsigned int hist_dist = mrpt::abs_diff(
                                m_last_selected_sector,
                                i);  // It's fine here to consider that -PI is far from +PI.
 
                        if (hist_dist >= options.HYSTERESIS_SECTOR_COUNT)
                                scores[3] = square(
                                        1.0 -
                                        (hist_dist - options.HYSTERESIS_SECTOR_COUNT) /
                                                double(nDirs));
                        else
                                scores[3] = 1.0;
                }
                else
                {
                        scores[3] = 1.0;
                }
 
                // Factor #5: clearance to nearest obstacle along path
                // ------------------------------------------------------------------------------------------
                {
                        const double query_dist_norm = std::min(0.99, target_dist * 0.95);
                        const double avr_path_clearance = ni.clearance->getClearance(
                                i /*path index*/, query_dist_norm, true /*interpolate path*/);
                        const double point_clearance = ni.clearance->getClearance(
                                i /*path index*/, query_dist_norm, false /*interpolate path*/);
                        scores[4] = 0.5 * (avr_path_clearance + point_clearance);
                }
 
                // Save stats for debugging:
                for (int l = 0; l < NUM_FACTORS; l++) m_dirs_scores(i, l) = scores[l];
        }
 
        // Normalize factors?
        ASSERT_(options.factorNormalizeOrNot.size() == NUM_FACTORS);
        for (int l = 0; l < NUM_FACTORS; l++)
        {
                if (!options.factorNormalizeOrNot[l]) continue;
 
                const double mmax = m_dirs_scores.col(l).maxCoeff();
                const double mmin = m_dirs_scores.col(l).minCoeff();
                const double span = mmax - mmin;
                if (span <= .0) continue;
 
                m_dirs_scores.col(l).array() -= mmin;
                m_dirs_scores.col(l).array() /= span;
        }
 
        // Phase 1: average of PHASE1_FACTORS and thresholding:
        // ----------------------------------------------------------------------
        const unsigned int NUM_PHASES = options.PHASE_FACTORS.size();
        ASSERT_(NUM_PHASES >= 1);
 
        std::vector<double> weights_sum_phase(NUM_PHASES, .0),
                weights_sum_phase_inv(NUM_PHASES);
        for (unsigned int i = 0; i < NUM_PHASES; i++)
        {
                for (unsigned int l : options.PHASE_FACTORS[i])
                        weights_sum_phase[i] += options.factorWeights[l];
                ASSERT_(weights_sum_phase[i] > .0);
                weights_sum_phase_inv[i] = 1.0 / weights_sum_phase[i];
        }
 
        eo.phase_scores = std::vector<std::vector<double>>(
                NUM_PHASES, std::vector<double>(nDirs, .0));
        auto& phase_scores = eo.phase_scores;  // shortcut
        double last_phase_threshold = -1.0;  // don't threshold for the first phase
 
        for (unsigned int phase_idx = 0; phase_idx < NUM_PHASES; phase_idx++)
        {
                double phase_min = std::numeric_limits<double>::max(), phase_max = .0;
 
                for (unsigned int i = 0; i < nDirs; i++)
                {
                        double this_dir_eval = 0;
 
                        if (ni.obstacles[i] <
                                        options.TOO_CLOSE_OBSTACLE ||  // Too close to obstacles ?
                                (phase_idx > 0 &&
                                 phase_scores[phase_idx - 1][i] <
                                         last_phase_threshold)  // thresholding of the previous
                                // phase
                                )
                        {
                                this_dir_eval = .0;
                        }
                        else
                        {
                                // Weighted avrg of factors:
                                for (unsigned int l : options.PHASE_FACTORS[phase_idx])
                                        this_dir_eval +=
                                                options.factorWeights[l] *
                                                std::log(std::max(1e-6, m_dirs_scores(i, l)));
 
                                this_dir_eval *= weights_sum_phase_inv[phase_idx];
                                this_dir_eval = std::exp(this_dir_eval);
                        }
                        phase_scores[phase_idx][i] = this_dir_eval;
 
                        mrpt::keep_max(phase_max, phase_scores[phase_idx][i]);
                        mrpt::keep_min(phase_min, phase_scores[phase_idx][i]);
 
                }  // for each direction
 
                ASSERT_(options.PHASE_THRESHOLDS.size() == NUM_PHASES);
                ASSERT_(
                        options.PHASE_THRESHOLDS[phase_idx] > .0 &&
                        options.PHASE_THRESHOLDS[phase_idx] < 1.0);
 
                last_phase_threshold =
                        options.PHASE_THRESHOLDS[phase_idx] * phase_max +
                        (1.0 - options.PHASE_THRESHOLDS[phase_idx]) * phase_min;
        }  // end for each phase
 
        // Give a chance for a derived class to manipulate the final evaluations:
        auto& dirs_eval = *phase_scores.rbegin();
 
        postProcessDirectionEvaluations(dirs_eval, ni, target_idx);
 
        // Recalculate the threshold just in case the postProcess function above
        // changed things:
        {
                double phase_min = std::numeric_limits<double>::max(), phase_max = .0;
                for (unsigned int i = 0; i < nDirs; i++)
                {
                        mrpt::keep_max(phase_max, phase_scores[NUM_PHASES - 1][i]);
                        mrpt::keep_min(phase_min, phase_scores[NUM_PHASES - 1][i]);
                }
                last_phase_threshold =
                        options.PHASE_THRESHOLDS[NUM_PHASES - 1] * phase_max +
                        (1.0 - options.PHASE_THRESHOLDS[NUM_PHASES - 1]) * phase_min;
        }
 
        // Search for best direction:
 
        // Of those directions above "last_phase_threshold", keep the GAP with the
        // largest maximum value within;
        // then pick the MIDDLE point as the final selection.
        std::vector<TGap> gaps;
        int best_gap_idx = -1;
        int gap_idx_for_target_dir = -1;
        {
                bool inside_gap = false;
                for (unsigned int i = 0; i < nDirs; i++)
                {
                        const double val = dirs_eval[i];
                        if (val < last_phase_threshold)
                        {
                                if (inside_gap)
                                {
                                        // We just ended a gap:
                                        auto& active_gap = *gaps.rbegin();
                                        active_gap.k_to = i - 1;
                                        inside_gap = false;
                                }
                        }
                        else
                        {
                                // higher or EQUAL to the treshold (equal is important just in
                                // case we have a "flat" diagram...)
                                if (!inside_gap)
                                {
                                        // We just started a gap:
                                        TGap new_gap;
                                        new_gap.k_from = i;
                                        gaps.emplace_back(new_gap);
                                        inside_gap = true;
                                }
                        }
 
                        if (inside_gap)
                        {
                                auto& active_gap = *gaps.rbegin();
                                if (val >= active_gap.max_eval)
                                {
                                        active_gap.k_best_eval = i;
                                }
                                mrpt::keep_max(active_gap.max_eval, val);
                                mrpt::keep_min(active_gap.min_eval, val);
 
                                if (target_k == i)
                                {
                                        active_gap.contains_target_k = true;
                                        gap_idx_for_target_dir = gaps.size() - 1;
                                }
 
                                if (best_gap_idx == -1 || val > gaps[best_gap_idx].max_eval)
                                {
                                        best_gap_idx = gaps.size() - 1;
                                }
                        }
                }  // end for i
 
                // Handle the case where we end with an open, active gap:
                if (inside_gap)
                {
                        auto& active_gap = *gaps.rbegin();
                        active_gap.k_to = nDirs - 1;
                }
        }
 
        ASSERT_(!gaps.empty());
        ASSERT_(best_gap_idx >= 0 && best_gap_idx < int(gaps.size()));
 
        const TGap& best_gap = gaps[best_gap_idx];
 
        eo.best_eval = best_gap.max_eval;
 
        // Different qualitative situations:
        if (best_gap_idx == gap_idx_for_target_dir)  // Gap contains target, AND
        {
                // the way seems to have clearance enought:
                const auto cl_left =
                        mrpt::abs_diff(target_k, (unsigned int)best_gap.k_from);
                const auto cl_right =
                        mrpt::abs_diff(target_k, (unsigned int)best_gap.k_to);
 
                const auto smallest_clearance_in_k_units = std::min(cl_left, cl_right);
                const unsigned int clearance_threshold =
                        mrpt::round(options.clearance_threshold_ratio * nDirs);
 
                const unsigned int gap_width = best_gap.k_to - best_gap.k_from;
                const unsigned int width_threshold =
                        mrpt::round(options.gap_width_ratio_threshold * nDirs);
 
                // Move straight to target?
                if (smallest_clearance_in_k_units >= clearance_threshold &&
                        gap_width >= width_threshold &&
                        ni.obstacles[target_k] > target_dist * 1.01)
                {
                        eo.best_k = target_k;
                }
        }
 
        if (eo.best_k == INVALID_K)  // did not fulfill conditions above
        {
                // Not heading to target: go thru the "middle" of the gap to maximize
                // clearance
                eo.best_k = mrpt::round(0.5 * (best_gap.k_to + best_gap.k_from));
        }
 
        // Alternative, simpler method to decide motion:
        // If target can be reached without collision *and* with a minimum of
        // clearance,
        // then select that direction, with the score as computed with the regular
        // formulas above
        // (even if that score was not the maximum!).
        if (target_dist < 0.99 &&
                (
                        /* No obstacles to target + enough clearance: */
                        (ni.obstacles[target_k] > target_dist * 1.01 &&
                         ni.clearance->getClearance(
                                 target_k /*path index*/, std::min(0.99, target_dist * 0.95),
                                 true /*interpolate path*/) > options.TOO_CLOSE_OBSTACLE) ||
                        /* Or: no obstacles to target with extra margin, target is really
                           near, dont check clearance: */
                        (ni.obstacles[target_k] >
                                 (target_dist + 0.15 /*meters*/ / ptg_ref_dist) &&
                         target_dist < (1.5 /*meters*/ / ptg_ref_dist))) &&
                dirs_eval[target_k] >
                        0 /* the direct target direction has at least a minimum score */
                )
        {
                eo.best_k = target_k;
                eo.best_eval = dirs_eval[target_k];
 
                // Reflect this decision in the phase score plots:
                phase_scores[NUM_PHASES - 1][target_k] += 2.0;
        }
}
 
void CHolonomicFullEval::navigate(const NavInput& ni, NavOutput& no)
{
        using mrpt::square;
 
        ASSERT_(ni.clearance != nullptr);
        ASSERT_(!ni.targets.empty());
 
        // Create a log record for returning data.
        CLogFileRecord_FullEval::Ptr log =
                mrpt::make_aligned_shared<CLogFileRecord_FullEval>();
        no.logRecord = log;
 
        const size_t numTrgs = ni.targets.size();
 
        std::vector<EvalOutput> evals(numTrgs);
        double best_eval = .0;
        unsigned int best_trg_idx = 0;
 
        for (unsigned int trg_idx = 0; trg_idx < numTrgs; trg_idx++)
        {
                auto& eo = evals[trg_idx];
                evalSingleTarget(trg_idx, ni, eo);
 
                if (eo.best_eval >=
                        best_eval)  // >= because we prefer the most advanced targets...
                {
                        best_eval = eo.best_eval;
                        best_trg_idx = trg_idx;
                }
        }
 
        // Prepare NavigationOutput data:
        if (best_eval == .0)
        {
                // No way found!
                no.desiredDirection = 0;
                no.desiredSpeed = 0;
        }
        else
        {
                // A valid movement:
                const auto ptg = getAssociatedPTG();
                const double ptg_ref_dist = ptg ? ptg->getRefDistance() : 1.0;
 
                no.desiredDirection = CParameterizedTrajectoryGenerator::index2alpha(
                        evals[best_trg_idx].best_k, ni.obstacles.size());
 
                // Speed control: Reduction factors
                // ---------------------------------------------
                const double targetNearnessFactor =
                        m_enableApproachTargetSlowDown
                                ? std::min(
                                          1.0, ni.targets[best_trg_idx].norm() /
                                                           (options.TARGET_SLOW_APPROACHING_DISTANCE /
                                                                ptg_ref_dist))
                                : 1.0;
 
                const double obs_dist =
                        ni.obstacles[evals[best_trg_idx].best_k];  // Was: min with
                // obs_clearance too.
                const double obs_dist_th = std::max(
                        options.TOO_CLOSE_OBSTACLE,
                        (options.OBSTACLE_SLOW_DOWN_DISTANCE / ptg_ref_dist) *
                                ni.maxObstacleDist);
                double riskFactor = 1.0;
                if (obs_dist <= options.TOO_CLOSE_OBSTACLE)
                {
                        riskFactor = 0.0;
                }
                else if (
                        obs_dist < obs_dist_th && obs_dist_th > options.TOO_CLOSE_OBSTACLE)
                {
                        riskFactor = (obs_dist - options.TOO_CLOSE_OBSTACLE) /
                                                 (obs_dist_th - options.TOO_CLOSE_OBSTACLE);
                }
                no.desiredSpeed =
                        ni.maxRobotSpeed * std::min(riskFactor, targetNearnessFactor);
        }
 
        m_last_selected_sector = evals[best_trg_idx].best_k;
 
        // LOG --------------------------
        if (log)
        {
                log->selectedTarget = best_trg_idx;
                log->selectedSector = evals[best_trg_idx].best_k;
                log->evaluation = evals[best_trg_idx].best_eval;
                log->dirs_eval = evals[best_trg_idx].phase_scores;
 
                if (options.LOG_SCORE_MATRIX)
                {
                        log->dirs_scores = m_dirs_scores;
                }
        }
}
 
unsigned int CHolonomicFullEval::direction2sector(
        const double a, const unsigned int N)
{
        const int idx = round(0.5 * (N * (1 + mrpt::math::wrapToPi(a) / M_PI) - 1));
        if (idx < 0)
                return 0;
        else
                return static_cast<unsigned int>(idx);
}
 
CLogFileRecord_FullEval::CLogFileRecord_FullEval()
        : selectedSector(0), evaluation(.0), dirs_scores(), selectedTarget(0)
{
}
 
uint8_t CLogFileRecord_FullEval::serializeGetVersion() const { return 3; }
void CLogFileRecord_FullEval::serializeTo(
        mrpt::serialization::CArchive& out) const
{
        out << CHolonomicLogFileRecord::dirs_eval << dirs_scores << selectedSector
                << evaluation << selectedTarget /*v3*/;
}
 
/*---------------------------------------------------------------
                                        readFromStream
  ---------------------------------------------------------------*/
void CLogFileRecord_FullEval::serializeFrom(
        mrpt::serialization::CArchive& in, uint8_t version)
{
        switch (version)
        {
                case 0:
                case 1:
                case 2:
                case 3:
                {
                        if (version >= 2)
                        {
                                in >> CHolonomicLogFileRecord::dirs_eval;
                        }
                        else
                        {
                                CHolonomicLogFileRecord::dirs_eval.resize(2);
                                in >> CHolonomicLogFileRecord::dirs_eval[0];
                                if (version >= 1)
                                {
                                        in >> CHolonomicLogFileRecord::dirs_eval[1];
                                }
                        }
                        in >> dirs_scores >> selectedSector >> evaluation;
                        if (version >= 3)
                        {
                                in >> selectedTarget;
                        }
                        else
                        {
                                selectedTarget = 0;
                        }
                }
                break;
                default:
                        MRPT_THROW_UNKNOWN_SERIALIZATION_VERSION(version)
        };
}
 
/*---------------------------------------------------------------
                                                TOptions
  ---------------------------------------------------------------*/
CHolonomicFullEval::TOptions::TOptions()
        :  // Default values:
          TOO_CLOSE_OBSTACLE(0.15),
          TARGET_SLOW_APPROACHING_DISTANCE(0.60),
          OBSTACLE_SLOW_DOWN_DISTANCE(0.15),
          HYSTERESIS_SECTOR_COUNT(5),
          factorWeights{0.1, 0.5, 0.5, 0.01, 1},
          factorNormalizeOrNot{0, 0, 0, 0, 1},
          PHASE_FACTORS{{1, 2}, {4}, {0, 2}},
          PHASE_THRESHOLDS{0.5, 0.6, 0.7},
          LOG_SCORE_MATRIX(false),
          clearance_threshold_ratio(0.05),
          gap_width_ratio_threshold(0.25)
{
}
 
void CHolonomicFullEval::TOptions::loadFromConfigFile(
        const mrpt::config::CConfigFileBase& c, const std::string& s)
{
        MRPT_START
 
        // Load from config text:
        MRPT_LOAD_CONFIG_VAR(TOO_CLOSE_OBSTACLE, double, c, s);
        MRPT_LOAD_CONFIG_VAR(TARGET_SLOW_APPROACHING_DISTANCE, double, c, s);
        MRPT_LOAD_CONFIG_VAR(OBSTACLE_SLOW_DOWN_DISTANCE, double, c, s);
        MRPT_LOAD_CONFIG_VAR(HYSTERESIS_SECTOR_COUNT, double, c, s);
        MRPT_LOAD_CONFIG_VAR(LOG_SCORE_MATRIX, bool, c, s);
        MRPT_LOAD_CONFIG_VAR(clearance_threshold_ratio, double, c, s);
        MRPT_LOAD_CONFIG_VAR(gap_width_ratio_threshold, double, c, s);
 
        c.read_vector(
                s, "factorWeights", std::vector<double>(), factorWeights, true);
        ASSERT_(factorWeights.size() == 5);
 
        c.read_vector(
                s, "factorNormalizeOrNot", factorNormalizeOrNot, factorNormalizeOrNot);
        ASSERT_(factorNormalizeOrNot.size() == factorWeights.size());
 
        // Phases:
        int PHASE_COUNT = 0;
        MRPT_LOAD_CONFIG_VAR_NO_DEFAULT(PHASE_COUNT, int, c, s);
 
        PHASE_FACTORS.resize(PHASE_COUNT);
        PHASE_THRESHOLDS.resize(PHASE_COUNT);
        for (int i = 0; i < PHASE_COUNT; i++)
        {
                c.read_vector(
                        s, mrpt::format("PHASE%i_FACTORS", i + 1), PHASE_FACTORS[i],
                        PHASE_FACTORS[i], true);
                ASSERT_(!PHASE_FACTORS[i].empty());
 
                PHASE_THRESHOLDS[i] = c.read_double(
                        s, mrpt::format("PHASE%i_THRESHOLD", i + 1), .0, true);
                ASSERT_(PHASE_THRESHOLDS[i] >= .0 && PHASE_THRESHOLDS[i] <= 1.0);
        }
 
        MRPT_END
}
 
void CHolonomicFullEval::TOptions::saveToConfigFile(
        mrpt::config::CConfigFileBase& c, const std::string& s) const
{
        MRPT_START;
 
        const int WN = mrpt::config::MRPT_SAVE_NAME_PADDING(),
                          WV = mrpt::config::MRPT_SAVE_VALUE_PADDING();
 
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                TOO_CLOSE_OBSTACLE,
                "Directions with collision-free distances below this threshold are not "
                "elegible.");
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                TARGET_SLOW_APPROACHING_DISTANCE,
                "Start to reduce speed when closer than this to target.");
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                OBSTACLE_SLOW_DOWN_DISTANCE,
                "Start to reduce speed when clearance is below this value ([0,1] ratio "
                "wrt obstacle reference/max distance)");
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                HYSTERESIS_SECTOR_COUNT,
                "Range of `sectors` (directions) for hysteresis over successive "
                "timesteps");
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                LOG_SCORE_MATRIX, "Save the entire score matrix in log files");
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                clearance_threshold_ratio,
                "Ratio [0,1], times path_count, gives the minimum number of paths at "
                "each side of a target direction to be accepted as desired direction");
        MRPT_SAVE_CONFIG_VAR_COMMENT(
                gap_width_ratio_threshold,
                "Ratio [0,1], times path_count, gives the minimum gap width to accept "
                "a direct motion towards target.");
 
        ASSERT_EQUAL_(factorWeights.size(), 5);
        c.write(
                s, "factorWeights",
                mrpt::system::sprintf_container("%.2f ", factorWeights), WN, WV,
                "[0]=Free space, [1]=Dist. in sectors, [2]=Closer to target "
                "(Euclidean), [3]=Hysteresis, [4]=clearance along path");
        c.write(
                s, "factorNormalizeOrNot",
                mrpt::system::sprintf_container("%u ", factorNormalizeOrNot), WN, WV,
                "Normalize factors or not (1/0)");
 
        c.write(
                s, "PHASE_COUNT", PHASE_FACTORS.size(), WN, WV,
                "Number of evaluation phases to run (params for each phase below)");
 
        for (unsigned int i = 0; i < PHASE_FACTORS.size(); i++)
        {
                c.write(
                        s, mrpt::format("PHASE%u_THRESHOLD", i + 1), PHASE_THRESHOLDS[i],
                        WN, WV,
                        "Phase scores must be above this relative range threshold [0,1] to "
                        "be considered in next phase (Default:`0.75`)");
                c.write(
                        s, mrpt::format("PHASE%u_FACTORS", i + 1),
                        mrpt::system::sprintf_container("%d ", PHASE_FACTORS[i]), WN, WV,
                        "Indices of the factors above to be considered in this phase");
        }
 
        MRPT_END;
}
 
uint8_t CHolonomicFullEval::serializeGetVersion() const { return 4; }
void CHolonomicFullEval::serializeTo(mrpt::serialization::CArchive& out) const
{
        // Params:
        out << options.factorWeights << options.HYSTERESIS_SECTOR_COUNT
                << options.PHASE_FACTORS <<  // v3
                options.TARGET_SLOW_APPROACHING_DISTANCE << options.TOO_CLOSE_OBSTACLE
                << options.PHASE_THRESHOLDS  // v3
                << options.OBSTACLE_SLOW_DOWN_DISTANCE  // v1
                << options.factorNormalizeOrNot  // v2
                << options.clearance_threshold_ratio
                << options.gap_width_ratio_threshold  // v4:
                ;
        // State:
        out << m_last_selected_sector;
}
void CHolonomicFullEval::serializeFrom(
        mrpt::serialization::CArchive& in, uint8_t version)
{
        switch (version)
        {
                case 0:
                case 1:
                case 2:
                case 3:
                case 4:
                {
                        // Params:
                        in >> options.factorWeights >> options.HYSTERESIS_SECTOR_COUNT;
 
                        if (version >= 3)
                        {
                                in >> options.PHASE_FACTORS;
                        }
                        else
                        {
                                options.PHASE_THRESHOLDS.resize(2);
                                in >> options.PHASE_FACTORS[0] >> options.PHASE_FACTORS[1];
                        }
                        in >> options.TARGET_SLOW_APPROACHING_DISTANCE >>
                                options.TOO_CLOSE_OBSTACLE;
 
                        if (version >= 3)
                        {
                                in >> options.PHASE_THRESHOLDS;
                        }
                        else
                        {
                                options.PHASE_THRESHOLDS.resize(1);
                                in >> options.PHASE_THRESHOLDS[0];
                        }
 
                        if (version >= 1) in >> options.OBSTACLE_SLOW_DOWN_DISTANCE;
                        if (version >= 2) in >> options.factorNormalizeOrNot;
 
                        if (version >= 4)
                        {
                                in >> options.clearance_threshold_ratio >>
                                        options.gap_width_ratio_threshold;
                        }
 
                        // State:
                        in >> m_last_selected_sector;
                }
                break;
                default:
                        MRPT_THROW_UNKNOWN_SERIALIZATION_VERSION(version)
        };
}
 
void CHolonomicFullEval::postProcessDirectionEvaluations(
        std::vector<double>& dir_evals, const NavInput& ni, unsigned int trg_idx)
{
        // Default: do nothing
}