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refactor(autoware_mpc_lateral_controller): speed up matrix calculation with sparse matrix #8908

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refactor(autoware_mpc_lateral_controller): speed up matrix calculation with sparse matrix #8908

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66 changes: 47 additions & 19 deletions control/autoware_mpc_lateral_controller/src/mpc.cpp
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// Copyright 2018-2021 The Autoware Foundation

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CodeScene Delta Analysis / CodeScene Cloud Delta Analysis (main)

ℹ Getting worse: Overall Code Complexity 

The mean cyclomatic complexity increases from 5.11 to 5.16, threshold = 4. This file has many conditional statements (e.g. if, for, while) across its implementation, leading to lower code health. Avoid adding more conditionals.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
Expand All @@ -20,6 +20,8 @@
#include "interpolation/linear_interpolation.hpp"
#include "rclcpp/rclcpp.hpp"

#include <Eigen/Sparse>

#include <algorithm>
#include <limits>

Expand Down Expand Up @@ -576,22 +578,47 @@

const int DIM_U_N = m_param.prediction_horizon * m_vehicle_model_ptr->getDimU();

Eigen::SparseMatrix<double> Cex_sparse = m.Cex.sparseView();
Eigen::SparseMatrix<double> Bex_sparse = m.Bex.sparseView();
Eigen::SparseMatrix<double> Qex_sparse = m.Qex.sparseView();
Eigen::SparseMatrix<double> R1ex_sparse = m.R1ex.sparseView();
Eigen::SparseMatrix<double> R2ex_sparse = m.R2ex.sparseView();

// cost function: 1/2 * Uex' * H * Uex + f' * Uex, H = B' * C' * Q * C * B + R
const MatrixXd CB = m.Cex * m.Bex;
const MatrixXd QCB = m.Qex * CB;
// MatrixXd H = CB.transpose() * QCB + m.R1ex + m.R2ex; // This calculation is heavy. looking for
// a good way. //NOLINT
MatrixXd H = MatrixXd::Zero(DIM_U_N, DIM_U_N);
H.triangularView<Eigen::Upper>() = CB.transpose() * QCB;
H.triangularView<Eigen::Upper>() += m.R1ex + m.R2ex;
H.triangularView<Eigen::Lower>() = H.transpose();
MatrixXd f = (m.Cex * (m.Aex * x0 + m.Wex)).transpose() * QCB - m.Uref_ex.transpose() * m.R1ex;
Eigen::SparseMatrix<double> CB_sparse(DIM_U_N, DIM_U_N);
CB_sparse = Cex_sparse * Bex_sparse;

Eigen::SparseMatrix<double> QCB_sparse(DIM_U_N, DIM_U_N);
QCB_sparse = Qex_sparse * CB_sparse;

Eigen::SparseMatrix<double> H_sparse(DIM_U_N, DIM_U_N);
{
Eigen::SparseMatrix<double> temp_sparse(DIM_U_N, DIM_U_N);
temp_sparse = CB_sparse.transpose() * QCB_sparse;

H_sparse = temp_sparse + R1ex_sparse + R2ex_sparse;
}

VectorXd f = VectorXd::Zero(DIM_U_N);
{
VectorXd CAW = VectorXd::Zero(Cex_sparse.rows());
CAW = Cex_sparse * (m.Aex * x0 + m.Wex);

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Redundant initialization for 'CAW'. The initialized value is overwritten before it is read. [redundantInitialization]

f = QCB_sparse.transpose() * CAW;

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Redundant initialization for 'f'. The initialized value is overwritten before it is read. [redundantInitialization]
f -= m.Uref_ex.transpose() * R1ex_sparse;
}

addSteerWeightF(prediction_dt, f);

MatrixXd A = MatrixXd::Identity(DIM_U_N, DIM_U_N);
for (int i = 1; i < DIM_U_N; i++) {
A(i, i - 1) = -1.0;
Eigen::SparseMatrix<double> A_sparse(DIM_U_N, DIM_U_N);
std::vector<Eigen::Triplet<double>> triplets;
for (int i = 0; i < DIM_U_N; ++i) {
triplets.emplace_back(i, i, 1.0);
if (i > 0) {
triplets.emplace_back(i, i - 1, -1.0);
}
}
A_sparse.setFromTriplets(triplets.begin(), triplets.end());

// steering angle limit
VectorXd lb = VectorXd::Constant(DIM_U_N, -m_steer_lim); // min steering angle
Expand All @@ -605,7 +632,8 @@
lbA(0) = m_raw_steer_cmd_prev - steer_rate_limits(0) * m_ctrl_period;

auto t_start = std::chrono::system_clock::now();
bool solve_result = m_qpsolver_ptr->solve(H, f.transpose(), A, lb, ub, lbA, ubA, Uex);
bool solve_result =
m_qpsolver_ptr->solve(H_sparse, f.transpose(), A_sparse, lb, ub, lbA, ubA, Uex);
auto t_end = std::chrono::system_clock::now();
if (!solve_result) {
warn_throttle("qp solver error");
Expand Down Expand Up @@ -667,16 +695,16 @@
}
}

void MPC::addSteerWeightF(const double prediction_dt, MatrixXd & f) const
void MPC::addSteerWeightF(const double prediction_dt, VectorXd & f) const
{
if (f.rows() < 2) {
if (f.size() < 2) {
return;
}

const double DT = prediction_dt;

// steer rate for i = 0
f(0, 0) += -2.0 * m_param.nominal_weight.steer_rate / (std::pow(DT, 2)) * 0.5;
f(0) += -2.0 * m_param.nominal_weight.steer_rate / (std::pow(DT, 2)) * 0.5;

// const double steer_acc_r = m_param.weight_steer_acc / std::pow(DT, 4);
const double steer_acc_r_cp1 =
Expand All @@ -686,11 +714,11 @@
const double steer_acc_r_cp4 = m_param.nominal_weight.steer_acc / std::pow(m_ctrl_period, 4);

// steer acc i = 0
f(0, 0) += ((-2.0 * m_raw_steer_cmd_prev + m_raw_steer_cmd_pprev) * steer_acc_r_cp4) * 0.5;
f(0) += ((-2.0 * m_raw_steer_cmd_prev + m_raw_steer_cmd_pprev) * steer_acc_r_cp4) * 0.5;

// steer acc for i = 1
f(0, 0) += (-2.0 * m_raw_steer_cmd_prev * (steer_acc_r_cp1 + steer_acc_r_cp2)) * 0.5;
f(0, 1) += (2.0 * m_raw_steer_cmd_prev * steer_acc_r_cp1) * 0.5;
f(0) += (-2.0 * m_raw_steer_cmd_prev * (steer_acc_r_cp1 + steer_acc_r_cp2)) * 0.5;
f(1) += (2.0 * m_raw_steer_cmd_prev * steer_acc_r_cp1) * 0.5;
}

double MPC::getPredictionDeltaTime(
Expand Down
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