| Directory: | ./ |
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| File: | src/path-optimization/spline-gradient-based/cost.hh |
| Date: | 2024-12-13 16:14:03 |
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| 1 | // Copyright (c) 2017, Joseph Mirabel | ||
| 2 | // Authors: Joseph Mirabel (joseph.mirabel@laas.fr) | ||
| 3 | // | ||
| 4 | |||
| 5 | // Redistribution and use in source and binary forms, with or without | ||
| 6 | // modification, are permitted provided that the following conditions are | ||
| 7 | // met: | ||
| 8 | // | ||
| 9 | // 1. Redistributions of source code must retain the above copyright | ||
| 10 | // notice, this list of conditions and the following disclaimer. | ||
| 11 | // | ||
| 12 | // 2. Redistributions in binary form must reproduce the above copyright | ||
| 13 | // notice, this list of conditions and the following disclaimer in the | ||
| 14 | // documentation and/or other materials provided with the distribution. | ||
| 15 | // | ||
| 16 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS | ||
| 17 | // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT | ||
| 18 | // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR | ||
| 19 | // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT | ||
| 20 | // HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, | ||
| 21 | // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT | ||
| 22 | // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, | ||
| 23 | // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY | ||
| 24 | // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| 25 | // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| 26 | // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH | ||
| 27 | // DAMAGE. | ||
| 28 | |||
| 29 | #ifndef HPP_CORE_PATH_OPTIMIZATION_SPLINE_GRADIENT_BASED_COST_HH | ||
| 30 | #define HPP_CORE_PATH_OPTIMIZATION_SPLINE_GRADIENT_BASED_COST_HH | ||
| 31 | |||
| 32 | #include <hpp/core/path-optimization/cost.hh> | ||
| 33 | #include <hpp/core/path/spline.hh> | ||
| 34 | #include <hpp/util/debug.hh> | ||
| 35 | #include <hpp/util/exception-factory.hh> | ||
| 36 | |||
| 37 | namespace hpp { | ||
| 38 | namespace core { | ||
| 39 | namespace pathOptimization { | ||
| 40 | /// TODO | ||
| 41 | /// The derivative of the cost is wrong when for freeflyer and planar | ||
| 42 | /// joints. It lacks the derivative of the difference operator. The issue | ||
| 43 | /// is that it is not a quadratic cost anymore. | ||
| 44 | template <typename _Spline> | ||
| 45 | struct HPP_CORE_LOCAL L2NormSquaredOfDerivative { | ||
| 46 | typedef _Spline Spline; | ||
| 47 | typedef typename Spline::Ptr_t SplinePtr_t; | ||
| 48 | typedef std::vector<SplinePtr_t> Splines_t; | ||
| 49 | |||
| 50 | 30 | L2NormSquaredOfDerivative(const Splines_t& splines, size_type paramSize, | |
| 51 | size_type paramDerivativeSize, | ||
| 52 | size_type derivativeOrder) | ||
| 53 |
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30 | : lambda_(splines.size()), |
| 54 | 30 | nSplines_(splines.size()), | |
| 55 | 30 | paramSize_(paramSize), | |
| 56 | 30 | paramDerivativeSize_(paramDerivativeSize), | |
| 57 | 30 | inputSize_(nSplines_ * Spline::NbCoeffs * paramSize), | |
| 58 | 30 | inputDerivativeSize_(nSplines_ * Spline::NbCoeffs * | |
| 59 | 30 | paramDerivativeSize), | |
| 60 | 30 | derivativeOrder_(derivativeOrder) { | |
| 61 |
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30 | assert(derivativeOrder_ > 0); |
| 62 | // Spline::NbPowerOfT = 2 * Spline::Order + 3 | ||
| 63 |
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30 | if (2 * derivativeOrder_ - 1 >= Spline::NbPowerOfT) { |
| 64 | ✗ | HPP_THROW(std::invalid_argument, | |
| 65 | "Cannot compute the squared norm of the " | ||
| 66 | << derivativeOrder_ | ||
| 67 | << "th order derivative with splines of order " | ||
| 68 | << Spline::Order); | ||
| 69 | } | ||
| 70 |
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30 | lambda_.setOnes(); |
| 71 | 30 | } | |
| 72 | |||
| 73 | ✗ | void computeLambdasFromSplineLength(const Splines_t& splines) { | |
| 74 | ✗ | for (std::size_t i = 0; i < nSplines_; ++i) | |
| 75 | ✗ | lambda_[i] = splines[i]->squaredNormIntegral(derivativeOrder_); | |
| 76 | ✗ | value_type lMax = lambda_.maxCoeff(); | |
| 77 | // Make sure there is no too relatively small values in lambda_. | ||
| 78 | ✗ | lambda_ = (lambda_.array() > 1e-6 * lMax) | |
| 79 | ✗ | .select(lambda_.cwiseInverse(), 1e6 / lMax); | |
| 80 | } | ||
| 81 | |||
| 82 | 132 | void value(value_type& result, const Splines_t& splines) const { | |
| 83 |
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132 | assert(nSplines_ == splines.size()); |
| 84 | 132 | result = 0; | |
| 85 |
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588 | for (std::size_t i = 0; i < nSplines_; ++i) |
| 86 | 456 | result += lambda_[i] * splines[i]->squaredNormIntegral(derivativeOrder_); | |
| 87 | 132 | } | |
| 88 | |||
| 89 | void jacobian(vectorOut_t J, const Splines_t& splines) const { | ||
| 90 | assert(nSplines_ == splines.size()); | ||
| 91 | assert(J.size() == inputDerivativeSize_); | ||
| 92 | size_type col = 0; | ||
| 93 | size_type size = Spline::NbCoeffs * paramDerivativeSize_; | ||
| 94 | for (std::size_t i = 0; i < nSplines_; ++i) { | ||
| 95 | splines[i]->squaredNormIntegralDerivative(derivativeOrder_, | ||
| 96 | J.segment(col, size)); | ||
| 97 | J.segment(col, size) *= lambda_[i]; | ||
| 98 | col += size; | ||
| 99 | } | ||
| 100 | } | ||
| 101 | |||
| 102 | 30 | void hessian(matrixOut_t H, const Splines_t& splines) const { | |
| 103 |
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30 | assert(H.rows() == inputDerivativeSize_); |
| 104 |
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30 | assert(H.cols() == inputDerivativeSize_); |
| 105 |
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30 | typename Spline::BasisFunctionIntegralMatrix_t Ic; |
| 106 | |||
| 107 |
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30 | H.setZero(); |
| 108 | |||
| 109 |
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132 | for (std::size_t k = 0; k < nSplines_; ++k) { |
| 110 |
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102 | splines[k]->squaredNormBasisFunctionIntegral(derivativeOrder_, Ic); |
| 111 |
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102 | Ic *= 2; |
| 112 | 102 | const size_type shift = k * Spline::NbCoeffs * paramSize_; | |
| 113 |
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618 | for (size_type i = 0; i < Spline::NbCoeffs; ++i) { |
| 114 |
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3500 | for (size_type j = 0; j < Spline::NbCoeffs; ++j) { |
| 115 | ✗ | H.block(shift + i * paramSize_, shift + j * paramSize_, paramSize_, | |
| 116 |
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2984 | paramSize_) |
| 117 | .diagonal() | ||
| 118 |
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2984 | .setConstant(Ic(i, j) * lambda_[k]); |
| 119 | } | ||
| 120 | } | ||
| 121 | |||
| 122 | #ifndef NDEBUG | ||
| 123 |
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204 | value_type res1 = 0.5 * splines[k]->rowParameters().transpose() * |
| 124 | ✗ | H.block(shift, shift, Spline::NbCoeffs * paramSize_, | |
| 125 |
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102 | Spline::NbCoeffs * paramSize_) * |
| 126 | 102 | splines[k]->rowParameters(); | |
| 127 | |||
| 128 | 102 | value_type res2 = | |
| 129 |
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102 | splines[k]->squaredNormIntegral(derivativeOrder_) * lambda_[k]; |
| 130 | |||
| 131 | 102 | value_type diff = res1 - res2; | |
| 132 | |||
| 133 | 102 | if (std::fabs(diff) > Eigen::NumTraits<value_type>::dummy_precision()) { | |
| 134 | hppDout(error, | ||
| 135 | "Hessian seems wrong for spline " | ||
| 136 | << k << ": " << res1 << " - " << res2 << " = " | ||
| 137 | << res1 - res2 << '\n' | ||
| 138 | << H.block(shift, shift, Spline::NbCoeffs * paramSize_, | ||
| 139 | Spline::NbCoeffs * paramSize_)); | ||
| 140 | } | ||
| 141 | #endif // NDEBUG | ||
| 142 | } | ||
| 143 | 30 | } | |
| 144 | |||
| 145 | vector_t lambda_; | ||
| 146 | const std::size_t nSplines_; | ||
| 147 | const size_type paramSize_, paramDerivativeSize_; | ||
| 148 | const size_type inputSize_, inputDerivativeSize_; | ||
| 149 | size_type derivativeOrder_; | ||
| 150 | }; | ||
| 151 | } // namespace pathOptimization | ||
| 152 | } // namespace core | ||
| 153 | } // namespace hpp | ||
| 154 | |||
| 155 | #endif // HPP_CORE_PATH_OPTIMIZATION_SPLINE_GRADIENT_BASED_COST_HH | ||
| 156 |