apollo::planning::DualVariableWarmStartOSQPInterface类 参考

#include <dual_variable_warm_start_osqp_interface.h>

apollo::planning::DualVariableWarmStartOSQPInterface 的协作图:

Public 成员函数
	DualVariableWarmStartOSQPInterface (size_t horizon, double ts, const Eigen::MatrixXd &ego, const Eigen::MatrixXi &obstacles_edges_num, const size_t obstacles_num, const Eigen::MatrixXd &obstacles_A, const Eigen::MatrixXd &obstacles_b, const Eigen::MatrixXd &xWS, const PlannerOpenSpaceConfig &planner_open_space_config)
virtual	~DualVariableWarmStartOSQPInterface ()=default
void	get_optimization_results (Eigen::MatrixXd *l_warm_up, Eigen::MatrixXd *n_warm_up) const
bool	optimize ()
void	assemble_P (std::vector< c_float > *P_data, std::vector< c_int > *P_indices, std::vector< c_int > *P_indptr)
void	assemble_constraint (std::vector< c_float > *A_data, std::vector< c_int > *A_indices, std::vector< c_int > *A_indptr)
void	assembleA (const int r, const int c, const std::vector< c_float > &P_data, const std::vector< c_int > &P_indices, const std::vector< c_int > &P_indptr)
void	check_solution (const Eigen::MatrixXd &l_warm_up, const Eigen::MatrixXd &n_warm_up)
	DualVariableWarmStartOSQPInterface (size_t horizon, double ts, const Eigen::MatrixXd &ego, const Eigen::MatrixXi &obstacles_edges_num, const size_t obstacles_num, const Eigen::MatrixXd &obstacles_A, const Eigen::MatrixXd &obstacles_b, const Eigen::MatrixXd &xWS, const DualVariableWarmStartConfig &DualVariableWarmStartConfig)

详细描述

在文件 dual_variable_warm_start_osqp_interface.h 第 37 行定义.

构造及析构函数说明

DualVariableWarmStartOSQPInterface() [1/2]

apollo::planning::DualVariableWarmStartOSQPInterface::DualVariableWarmStartOSQPInterface	(	size_t	horizon,
		double	ts,
		const Eigen::MatrixXd &	ego,
		const Eigen::MatrixXi &	obstacles_edges_num,
		const size_t	obstacles_num,
		const Eigen::MatrixXd &	obstacles_A,
		const Eigen::MatrixXd &	obstacles_b,
		const Eigen::MatrixXd &	xWS,
		const PlannerOpenSpaceConfig &	planner_open_space_config
	)

在文件 dual_variable_warm_start_osqp_interface.cc 第 32 行定义.

    : ts_(ts),
      ego_(ego),
      obstacles_edges_num_(obstacles_edges_num),
      obstacles_A_(obstacles_A),
      obstacles_b_(obstacles_b),
      xWS_(xWS) {
  ACHECK(horizon < std::numeric_limits<int>::max())
      << "Invalid cast on horizon in open space planner";
  horizon_ = static_cast<int>(horizon);
  ACHECK(obstacles_num < std::numeric_limits<int>::max())
      << "Invalid cast on obstacles_num in open space planner";
  obstacles_num_ = static_cast<int>(obstacles_num);
  w_ev_ = ego_(1, 0) + ego_(3, 0);
  l_ev_ = ego_(0, 0) + ego_(2, 0);
  g_ = {l_ev_ / 2, w_ev_ / 2, l_ev_ / 2, w_ev_ / 2};
  offset_ = (ego_(0, 0) + ego_(2, 0)) / 2 - ego_(2, 0);
  obstacles_edges_sum_ = obstacles_edges_num_.sum();
  l_start_index_ = 0;
  n_start_index_ = l_start_index_ + obstacles_edges_sum_ * (horizon_ + 1);
  l_warm_up_ = Eigen::MatrixXd::Zero(obstacles_edges_sum_, horizon_ + 1);
  n_warm_up_ = Eigen::MatrixXd::Zero(4 * obstacles_num_, horizon_ + 1);
 
  // get_nlp_info
  lambda_horizon_ = obstacles_edges_sum_ * (horizon_ + 1);
 
  miu_horizon_ = obstacles_num_ * 4 * (horizon_ + 1);
 
  // number of variables
  num_of_variables_ = lambda_horizon_ + miu_horizon_;
  // number of constraints
  num_of_constraints_ = 3 * obstacles_num_ * (horizon_ + 1) + num_of_variables_;
 
  min_safety_distance_ =
      planner_open_space_config.dual_variable_warm_start_config()
          .min_safety_distance();
  check_mode_ =
      planner_open_space_config.dual_variable_warm_start_config().debug_osqp();
  osqp_config_ =
      planner_open_space_config.dual_variable_warm_start_config().osqp_config();
}

~DualVariableWarmStartOSQPInterface()

virtual apollo::planning::DualVariableWarmStartOSQPInterface::~DualVariableWarmStartOSQPInterface ( )

virtualdefault

DualVariableWarmStartOSQPInterface() [2/2]

apollo::planning::DualVariableWarmStartOSQPInterface::DualVariableWarmStartOSQPInterface	(	size_t	horizon,
		double	ts,
		const Eigen::MatrixXd &	ego,
		const Eigen::MatrixXi &	obstacles_edges_num,
		const size_t	obstacles_num,
		const Eigen::MatrixXd &	obstacles_A,
		const Eigen::MatrixXd &	obstacles_b,
		const Eigen::MatrixXd &	xWS,
		const DualVariableWarmStartConfig &	DualVariableWarmStartConfig
	)

成员函数说明

assemble_constraint()

void apollo::planning::DualVariableWarmStartOSQPInterface::assemble_constraint	(	std::vector< c_float > *	A_data,
		std::vector< c_int > *	A_indices,
		std::vector< c_int > *	A_indptr
	)

在文件 dual_variable_warm_start_osqp_interface.cc 第 362 行定义.

                                {
  /*
   * The constraint matrix is as the form,
   *  |R' * A',   G'|, #: 2 * obstacles_num_ * (horizon_ + 1)
   *  |A * t - b, -g|, #: obstacles_num_ * (horizon_ + 1)
   *  |I,          0|, #: num_of_lambda
   *  |0,          I|, #: num_of_miu
   */
  int r1_index = 0;
  int r2_index = 2 * obstacles_num_ * (horizon_ + 1);
  int r3_index = 3 * obstacles_num_ * (horizon_ + 1);
  int r4_index = 3 * obstacles_num_ * (horizon_ + 1) + lambda_horizon_;
  int first_row_location = 0;
 
  // lambda variables
  for (int i = 0; i < horizon_ + 1; ++i) {
    int edges_counter = 0;
 
    Eigen::MatrixXd R(2, 2);
    R << cos(xWS_(2, i)), sin(xWS_(2, i)), sin(xWS_(2, i)), cos(xWS_(2, i));
 
    Eigen::MatrixXd t_trans(1, 2);
    t_trans << (xWS_(0, i) + cos(xWS_(2, i)) * offset_),
        (xWS_(1, i) + sin(xWS_(2, i)) * offset_);
 
    // assume: stationary obstacles
    for (int j = 0; j < obstacles_num_; ++j) {
      int current_edges_num = obstacles_edges_num_(j, 0);
      Eigen::MatrixXd Aj =
          obstacles_A_.block(edges_counter, 0, current_edges_num, 2);
      Eigen::MatrixXd bj =
          obstacles_b_.block(edges_counter, 0, current_edges_num, 1);
 
      Eigen::MatrixXd r1_block(2, current_edges_num);
      r1_block = R * Aj.transpose();
 
      Eigen::MatrixXd r2_block(1, current_edges_num);
      r2_block = t_trans * Aj.transpose() - bj.transpose();
 
      // insert into A matrix, col by col
      for (int k = 0; k < current_edges_num; ++k) {
        A_data->emplace_back(r1_block(0, k));
        A_indices->emplace_back(r1_index);
 
        A_data->emplace_back(r1_block(1, k));
        A_indices->emplace_back(r1_index + 1);
 
        A_data->emplace_back(r2_block(0, k));
        A_indices->emplace_back(r2_index);
 
        A_data->emplace_back(1.0);
        A_indices->emplace_back(r3_index);
        r3_index++;
 
        A_indptr->emplace_back(first_row_location);
        first_row_location += 4;
      }
 
      // Update index
      edges_counter += current_edges_num;
      r1_index += 2;
      r2_index += 1;
    }
  }
 
  // miu variables
  // G: ((1, 0, -1, 0), (0, 1, 0, -1))
  // g: g_
  r1_index = 0;
  r2_index = 2 * obstacles_num_ * (horizon_ + 1);
  for (int i = 0; i < horizon_ + 1; ++i) {
    for (int j = 0; j < obstacles_num_; ++j) {
      for (int k = 0; k < 4; ++k) {
        // update G
        if (k < 2) {
          A_data->emplace_back(1.0);
        } else {
          A_data->emplace_back(-1.0);
        }
        A_indices->emplace_back(r1_index + k % 2);
 
        // update g'
        A_data->emplace_back(-g_[k]);
        A_indices->emplace_back(r2_index);
 
        // update I
        A_data->emplace_back(1.0);
        A_indices->emplace_back(r4_index);
        r4_index++;
 
        // update col index
        A_indptr->emplace_back(first_row_location);
        first_row_location += 3;
      }
 
      // update index
      r1_index += 2;
      r2_index += 1;
    }
  }
 
  A_indptr->emplace_back(first_row_location);
 
  CHECK_EQ(A_data->size(), A_indices->size());
  CHECK_EQ(A_indptr->size(), static_cast<size_t>(num_of_variables_) + 1);
}

assemble_P()

void apollo::planning::DualVariableWarmStartOSQPInterface::assemble_P	(	std::vector< c_float > *	P_data,
		std::vector< c_int > *	P_indices,
		std::vector< c_int > *	P_indptr
	)

在文件 dual_variable_warm_start_osqp_interface.cc 第 299 行定义.

                                {
  // the objective function is norm(A' * lambda)
  std::vector<c_float> P_tmp;
  int edges_counter = 0;
 
  for (int j = 0; j < obstacles_num_; ++j) {
    int current_edges_num = obstacles_edges_num_(j, 0);
    Eigen::MatrixXd Aj;
    Aj = obstacles_A_.block(edges_counter, 0, current_edges_num, 2);
    // Eigen::MatrixXd AAj(current_edges_num, current_edges_num);
    Aj = Aj * Aj.transpose();
 
    CHECK_EQ(current_edges_num, Aj.cols());
    CHECK_EQ(current_edges_num, Aj.rows());
 
    for (int c = 0; c < current_edges_num; ++c) {
      for (int r = 0; r < current_edges_num; ++r) {
        P_tmp.emplace_back(Aj(r, c));
      }
    }
 
    // Update index
    edges_counter += current_edges_num;
  }
 
  int l_index = l_start_index_;
  int first_row_location = 0;
  // the objective function is norm(A' * lambda)
  for (int i = 0; i < horizon_ + 1; ++i) {
    edges_counter = 0;
 
    for (auto item : P_tmp) {
      P_data->emplace_back(item);
    }
    // current assume: stationary obstacles
    for (int j = 0; j < obstacles_num_; ++j) {
      int current_edges_num = obstacles_edges_num_(j, 0);
 
      for (int c = 0; c < current_edges_num; ++c) {
        P_indptr->emplace_back(first_row_location);
        for (int r = 0; r < current_edges_num; ++r) {
          P_indices->emplace_back(r + l_index);
        }
        first_row_location += current_edges_num;
      }
 
      // Update index
      edges_counter += current_edges_num;
      l_index += current_edges_num;
    }
  }
 
  CHECK_EQ(P_indptr->size(), static_cast<size_t>(lambda_horizon_));
  for (int i = lambda_horizon_; i < num_of_variables_ + 1; ++i) {
    P_indptr->emplace_back(first_row_location);
  }
 
  CHECK_EQ(P_data->size(), P_indices->size());
  CHECK_EQ(P_indptr->size(), static_cast<size_t>(num_of_variables_) + 1);
}

assembleA()

void apollo::planning::DualVariableWarmStartOSQPInterface::assembleA	(	const int	r,
		const int	c,
		const std::vector< c_float > &	P_data,
		const std::vector< c_int > &	P_indices,
		const std::vector< c_int > &	P_indptr
	)

在文件 dual_variable_warm_start_osqp_interface.cc 第 104 行定义.

                                                                         {
  constraint_A_ = Eigen::MatrixXf::Zero(r, c);
 
  for (size_t i = 0; i < P_indptr.size() - 1; ++i) {
    if (P_indptr[i] < 0 || P_indptr[i] >= static_cast<int>(P_indices.size())) {
      continue;
    }
 
    for (auto idx = P_indptr[i]; idx < P_indptr[i + 1]; ++idx) {
      int tmp_c = static_cast<int>(i);
      int tmp_r = static_cast<int>(P_indices[idx]);
      constraint_A_(tmp_r, tmp_c) = static_cast<float>(P_data[idx]);
    }
  }
}

check_solution()

void apollo::planning::DualVariableWarmStartOSQPInterface::check_solution	(	const Eigen::MatrixXd &	l_warm_up,
		const Eigen::MatrixXd &	n_warm_up
	)

在文件 dual_variable_warm_start_osqp_interface.cc 第 240 行定义.

                                                                    {
  Eigen::MatrixXf x(num_of_variables_, 1);
  Eigen::MatrixXf g(num_of_constraints_, 1);
 
  // extract primal results
  int variable_index = 0;
  // 1. lagrange constraint l, [0, obstacles_edges_sum_ - 1] * [0,
  // horizon_l
  for (int i = 0; i < horizon_ + 1; ++i) {
    for (int j = 0; j < obstacles_edges_sum_; ++j) {
      x(variable_index, 0) = static_cast<float>(l_warm_up(j, i));
      ++variable_index;
    }
  }
 
  // 2. lagrange constraint n, [0, 4*obstacles_num-1] * [0, horizon_]
  for (int i = 0; i < horizon_ + 1; ++i) {
    for (int j = 0; j < 4 * obstacles_num_; ++j) {
      x(variable_index, 0) = static_cast<float>(n_warm_up(j, i));
      ++variable_index;
    }
  }
 
  g = constraint_A_ * x;
  int r1_index = 0;
  int r2_index = 2 * obstacles_num_ * (horizon_ + 1);
  int r3_index = 3 * obstacles_num_ * (horizon_ + 1);
  int r4_index = 3 * obstacles_num_ * (horizon_ + 1) + lambda_horizon_;
 
  for (int idx = r1_index; idx < r2_index; ++idx) {
    if (std::abs(g(idx, 0)) > 1e-6) {
      AERROR << "G' * mu + R' * A * lambda == 0 constraint fails, "
             << "constraint_index: " << idx << ", g: " << g(idx, 0);
    }
  }
 
  for (int idx = r2_index; idx < r3_index; ++idx) {
    if (g(idx, 0) < min_safety_distance_) {
      AERROR << "-g' * mu + (A * t - b) * lambda) >= d_min constraint fails, "
             << "constraint_index: " << idx << ", g: " << g(idx, 0);
    }
  }
 
  for (int idx = r3_index; idx < r4_index; ++idx) {
    if (g(idx, 0) < 0) {
      AERROR << "lambda box constraint fails, "
             << "constraint_index: " << idx << ", g: " << g(idx, 0);
    }
  }
 
  for (int idx = r4_index; idx < num_of_constraints_; ++idx) {
    if (g(idx, 0) < 0) {
      AERROR << "miu box constraint fails, "
             << "constraint_index: " << idx << ", g: " << g(idx, 0);
    }
  }
}

get_optimization_results()

void apollo::planning::DualVariableWarmStartOSQPInterface::get_optimization_results	(	Eigen::MatrixXd *	l_warm_up,
		Eigen::MatrixXd *	n_warm_up
	)		const

在文件 dual_variable_warm_start_osqp_interface.cc 第 471 行定义.

                                                              {
  *l_warm_up = l_warm_up_;
  *n_warm_up = n_warm_up_;
}

optimize()

bool apollo::planning::DualVariableWarmStartOSQPInterface::optimize

(

)

在文件 dual_variable_warm_start_osqp_interface.cc 第 122 行定义.

                                                  {
  int kNumParam = num_of_variables_;
  int kNumConst = num_of_constraints_;
 
  bool succ = true;
  // assemble P, quadratic term in objective
  std::vector<c_float> P_data;
  std::vector<c_int> P_indices;
  std::vector<c_int> P_indptr;
  assemble_P(&P_data, &P_indices, &P_indptr);
  if (check_mode_) {
    AINFO << "print P_data in whole: ";
    printMatrix(kNumParam, kNumParam, P_data, P_indices, P_indptr);
  }
  // assemble q, linear term in objective
  c_float q[kNumParam];
  for (int i = 0; i < kNumParam; ++i) {
    q[i] = 0.0;
  }
 
  // assemble A, linear term in constraints
  std::vector<c_float> A_data;
  std::vector<c_int> A_indices;
  std::vector<c_int> A_indptr;
  assemble_constraint(&A_data, &A_indices, &A_indptr);
  if (check_mode_) {
    AINFO << "print A_data in whole: ";
    printMatrix(kNumConst, kNumParam, A_data, A_indices, A_indptr);
    assembleA(kNumConst, kNumParam, A_data, A_indices, A_indptr);
  }
 
  // assemble lb & ub
  c_float lb[kNumConst];
  c_float ub[kNumConst];
  for (int i = 0; i < kNumConst; ++i) {
    lb[i] = 0.0;
    if (i >= 2 * obstacles_num_ * (horizon_ + 1) &&
        i < 3 * obstacles_num_ * (horizon_ + 1)) {
      lb[i] = min_safety_distance_;
    }
    if (i < 2 * obstacles_num_ * (horizon_ + 1)) {
      ub[i] = 0.0;
    } else {
      ub[i] = 2e19;
    }
  }
 
  // Problem settings
  OSQPSettings* settings =
      reinterpret_cast<OSQPSettings*>(c_malloc(sizeof(OSQPSettings)));
 
  // Define Solver settings as default
  osqp_set_default_settings(settings);
  settings->alpha = osqp_config_.alpha();  // Change alpha parameter
  settings->eps_abs = osqp_config_.eps_abs();
  settings->eps_rel = osqp_config_.eps_rel();
  settings->max_iter = osqp_config_.max_iter();
  settings->polish = osqp_config_.polish();
  settings->verbose = osqp_config_.osqp_debug_log();
 
  // Populate data
  OSQPData* data = reinterpret_cast<OSQPData*>(c_malloc(sizeof(OSQPData)));
  data->n = kNumParam;
  data->m = kNumConst;
  data->P = csc_matrix(data->n, data->n, P_data.size(), P_data.data(),
                       P_indices.data(), P_indptr.data());
  data->q = q;
  data->A = csc_matrix(data->m, data->n, A_data.size(), A_data.data(),
                       A_indices.data(), A_indptr.data());
  data->l = lb;
  data->u = ub;
 
  // Workspace
  OSQPWorkspace* work = nullptr;
  // osqp_setup(&work, data, settings);
  work = osqp_setup(data, settings);
 
  // Solve Problem
  osqp_solve(work);
 
  // check state
  if (work->info->status_val != 1 && work->info->status_val != 2) {
    AWARN << "OSQP dual warm up unsuccess, "
          << "return status: " << work->info->status;
    succ = false;
  }
 
  // extract primal results
  int variable_index = 0;
  // 1. lagrange constraint l, [0, obstacles_edges_sum_ - 1] * [0,
  // horizon_l
  for (int i = 0; i < horizon_ + 1; ++i) {
    for (int j = 0; j < obstacles_edges_sum_; ++j) {
      l_warm_up_(j, i) = work->solution->x[variable_index];
      ++variable_index;
    }
  }
 
  // 2. lagrange constraint n, [0, 4*obstacles_num-1] * [0, horizon_]
  for (int i = 0; i < horizon_ + 1; ++i) {
    for (int j = 0; j < 4 * obstacles_num_; ++j) {
      n_warm_up_(j, i) = work->solution->x[variable_index];
      ++variable_index;
    }
  }
 
  succ = succ & (work->info->obj_val <= 1.0);
 
  // Cleanup
  osqp_cleanup(work);
  c_free(data->A);
  c_free(data->P);
  c_free(data);
  c_free(settings);
 
  return succ;
}

---

该类的文档由以下文件生成:

• modules/planning/planning_open_space/trajectory_smoother/dual_variable_warm_start_osqp_interface.h
• modules/planning/planning_open_space/trajectory_smoother/dual_variable_warm_start_osqp_interface.cc