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distance_approach_ipopt_fixed_ts_interface.h
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4 * Licensed under the Apache License, Version 2.0 (the "License");
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13 * See the License for the specific language governing permissions and
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16
17/*
18 * @file
19 */
20
21#pragma once
22
23#include <algorithm>
24#include <limits>
25#include <vector>
26
27#include <omp.h>
28
29#include <adolc/adolc.h>
30#include <adolc/adolc_openmp.h>
31#include <adolc/adolc_sparse.h>
32#include <adolc/adouble.h>
33
34#include <coin/IpTNLP.hpp>
35#include <coin/IpTypes.hpp>
36
37#include "Eigen/Dense"
38
39#include "modules/common_msgs/config_msgs/vehicle_config.pb.h"
40#include "modules/planning/planning_open_space/proto/planner_open_space_config.pb.h"
41
42#include "cyber/common/log.h"
43#include "cyber/common/macros.h"
49
50#define tag_f 1
51#define tag_g 2
52#define tag_L 3
53#define HPOFF 30
54
55namespace apollo {
56namespace planning {
57
59 public:
61 const size_t horizon, const double ts, const Eigen::MatrixXd& ego,
62 const Eigen::MatrixXd& xWS, const Eigen::MatrixXd& uWS,
63 const Eigen::MatrixXd& l_warm_up, const Eigen::MatrixXd& n_warm_up,
64 const Eigen::MatrixXd& x0, const Eigen::MatrixXd& xf,
65 const Eigen::MatrixXd& last_time_u, const std::vector<double>& XYbounds,
66 const Eigen::MatrixXi& obstacles_edges_num, const size_t obstacles_num,
67 const Eigen::MatrixXd& obstacles_A, const Eigen::MatrixXd& obstacles_b,
68 const PlannerOpenSpaceConfig& planner_open_space_config);
69
71
73 bool get_nlp_info(int& n, int& m, int& nnz_jac_g, int& nnz_h_lag, // NOLINT
74 IndexStyleEnum& index_style) override; // NOLINT
75
77 bool get_bounds_info(int n, double* x_l, double* x_u, int m, double* g_l,
78 double* g_u) override;
79
81 bool get_starting_point(int n, bool init_x, double* x, bool init_z,
82 double* z_L, double* z_U, int m, bool init_lambda,
83 double* lambda) override;
84
86 bool eval_f(int n, const double* x, bool new_x, double& obj_value) override;
87
89 bool eval_grad_f(int n, const double* x, bool new_x, double* grad_f) override;
90
92 bool eval_g(int n, const double* x, bool new_x, int m, double* g) override;
93
95 bool check_g(int n, const double* x, int m, const double* g);
96
101 bool eval_jac_g(int n, const double* x, bool new_x, int m, int nele_jac,
102 int* iRow, int* jCol, double* values) override;
103
104 // sequential implementation to jac_g
105 bool eval_jac_g_ser(int n, const double* x, bool new_x, int m, int nele_jac,
106 int* iRow, int* jCol, double* values) override;
107
113 bool eval_h(int n, const double* x, bool new_x, double obj_factor, int m,
114 const double* lambda, bool new_lambda, int nele_hess, int* iRow,
115 int* jCol, double* values) override;
116
120 void finalize_solution(Ipopt::SolverReturn status, int n, const double* x,
121 const double* z_L, const double* z_U, int m,
122 const double* g, const double* lambda,
123 double obj_value, const Ipopt::IpoptData* ip_data,
124 Ipopt::IpoptCalculatedQuantities* ip_cq) override;
125
126 void get_optimization_results(Eigen::MatrixXd* state_result,
127 Eigen::MatrixXd* control_result,
128 Eigen::MatrixXd* time_result,
129 Eigen::MatrixXd* dual_l_result,
130 Eigen::MatrixXd* dual_n_result) const override;
131
132 //*************** start ADOL-C part ***********************************
134 template <class T>
135 void eval_obj(int n, const T* x, T* obj_value);
136
138 template <class T>
139 void eval_constraints(int n, const T* x, int m, T* g);
140
142 void generate_tapes(int n, int m, int* nnz_jac_g, int* nnz_h_lag);
143 //*************** end ADOL-C part ***********************************
144
145 private:
146 int num_of_variables_ = 0;
147 int num_of_constraints_ = 0;
148 int horizon_ = 0;
149 int lambda_horizon_ = 0;
150 int miu_horizon_ = 0;
151 double ts_ = 0.0;
152 Eigen::MatrixXd ego_;
153 Eigen::MatrixXd xWS_;
154 Eigen::MatrixXd uWS_;
155 Eigen::MatrixXd l_warm_up_;
156 Eigen::MatrixXd n_warm_up_;
157 Eigen::MatrixXd x0_;
158 Eigen::MatrixXd xf_;
159 Eigen::MatrixXd last_time_u_;
160 std::vector<double> XYbounds_;
161
162 // debug flag
163 bool enable_constraint_check_;
164
165 // penalty
166 double weight_state_x_ = 0.0;
167 double weight_state_y_ = 0.0;
168 double weight_state_phi_ = 0.0;
169 double weight_state_v_ = 0.0;
170 double weight_input_steer_ = 0.0;
171 double weight_input_a_ = 0.0;
172 double weight_rate_steer_ = 0.0;
173 double weight_rate_a_ = 0.0;
174 double weight_stitching_steer_ = 0.0;
175 double weight_stitching_a_ = 0.0;
176 double weight_first_order_time_ = 0.0;
177 double weight_second_order_time_ = 0.0;
178
179 double w_ev_ = 0.0;
180 double l_ev_ = 0.0;
181 std::vector<double> g_;
182 double offset_ = 0.0;
183 Eigen::MatrixXi obstacles_edges_num_;
184 int obstacles_num_ = 0;
185 int obstacles_edges_sum_ = 0;
186 double wheelbase_ = 0.0;
187
188 Eigen::MatrixXd state_result_;
189 Eigen::MatrixXd dual_l_result_;
190 Eigen::MatrixXd dual_n_result_;
191 Eigen::MatrixXd control_result_;
192 Eigen::MatrixXd time_result_;
193
194 // obstacles_A
195 Eigen::MatrixXd obstacles_A_;
196
197 // obstacles_b
198 Eigen::MatrixXd obstacles_b_;
199
200 // whether to use fix time
201 bool use_fix_time_ = false;
202
203 // state start index
204 int state_start_index_ = 0;
205
206 // control start index.
207 int control_start_index_ = 0;
208
209 // time start index
210 int time_start_index_ = 0;
211
212 // lagrangian l start index
213 int l_start_index_ = 0;
214
215 // lagrangian n start index
216 int n_start_index_ = 0;
217
218 double min_safety_distance_ = 0.0;
219
220 double max_safety_distance_ = 0.0;
221
222 double max_steer_angle_ = 0.0;
223
224 double max_speed_forward_ = 0.0;
225
226 double max_speed_reverse_ = 0.0;
227
228 double max_acceleration_forward_ = 0.0;
229
230 double max_acceleration_reverse_ = 0.0;
231
232 double min_time_sample_scaling_ = 0.0;
233
234 double max_time_sample_scaling_ = 0.0;
235
236 double max_steer_rate_ = 0.0;
237
238 double max_lambda_ = 0.0;
239
240 double max_miu_ = 0.0;
241
242 private:
243 DistanceApproachConfig distance_approach_config_;
244 PlannerOpenSpaceConfig planner_open_space_config_;
245 const common::VehicleParam vehicle_param_ =
247
248 private:
249 //*************** start ADOL-C part ***********************************
250 double* obj_lam;
251 //** variables for sparsity exploitation
252 unsigned int* rind_g; /* row indices */
253 unsigned int* cind_g; /* column indices */
254 double* jacval; /* values */
255 unsigned int* rind_L; /* row indices */
256 unsigned int* cind_L; /* column indices */
257 double* hessval; /* values */
258 int nnz_jac;
259 int nnz_L;
260 int options_g[4];
261 int options_L[4];
262 //*************** end ADOL-C part ***********************************
263
264// park generic
265 public:
267 const size_t horizon, const double ts, const Eigen::MatrixXd& ego,
268 const Eigen::MatrixXd& xWS, const Eigen::MatrixXd& uWS,
269 const Eigen::MatrixXd& l_warm_up, const Eigen::MatrixXd& n_warm_up,
270 const Eigen::MatrixXd& x0, const Eigen::MatrixXd& xf,
271 const Eigen::MatrixXd& last_time_u, const std::vector<double>& XYbounds,
272 const Eigen::MatrixXi& obstacles_edges_num, const size_t obstacles_num,
273 const Eigen::MatrixXd& obstacles_A, const Eigen::MatrixXd& obstacles_b,
274 const DistanceApproachConfig& distance_approach_config);
275};
276
277} // namespace planning
278} // namespace apollo
static const VehicleConfig & GetConfig()
Get the current vehicle configuration.
bool eval_jac_g_ser(int n, const double *x, bool new_x, int m, int nele_jac, int *iRow, int *jCol, double *values) override
void eval_obj(int n, const T *x, T *obj_value)
Template to return the objective value
bool get_bounds_info(int n, double *x_l, double *x_u, int m, double *g_l, double *g_u) override
Method to return the bounds for my problem
bool eval_f(int n, const double *x, bool new_x, double &obj_value) override
Method to return the objective value
bool check_g(int n, const double *x, int m, const double *g)
Check unfeasible constraints for further study
DistanceApproachIPOPTFixedTsInterface(const size_t horizon, const double ts, const Eigen::MatrixXd &ego, const Eigen::MatrixXd &xWS, const Eigen::MatrixXd &uWS, const Eigen::MatrixXd &l_warm_up, const Eigen::MatrixXd &n_warm_up, const Eigen::MatrixXd &x0, const Eigen::MatrixXd &xf, const Eigen::MatrixXd &last_time_u, const std::vector< double > &XYbounds, const Eigen::MatrixXi &obstacles_edges_num, const size_t obstacles_num, const Eigen::MatrixXd &obstacles_A, const Eigen::MatrixXd &obstacles_b, const DistanceApproachConfig &distance_approach_config)
bool get_starting_point(int n, bool init_x, double *x, bool init_z, double *z_L, double *z_U, int m, bool init_lambda, double *lambda) override
Method to return the starting point for the algorithm
void eval_constraints(int n, const T *x, int m, T *g)
Template to compute constraints
void generate_tapes(int n, int m, int *nnz_jac_g, int *nnz_h_lag)
Method to generate the required tapes by ADOL-C
void get_optimization_results(Eigen::MatrixXd *state_result, Eigen::MatrixXd *control_result, Eigen::MatrixXd *time_result, Eigen::MatrixXd *dual_l_result, Eigen::MatrixXd *dual_n_result) const override
bool get_nlp_info(int &n, int &m, int &nnz_jac_g, int &nnz_h_lag, IndexStyleEnum &index_style) override
Method to return some info about the nlp
bool eval_g(int n, const double *x, bool new_x, int m, double *g) override
Method to return the constraint residuals
bool eval_h(int n, const double *x, bool new_x, double obj_factor, int m, const double *lambda, bool new_lambda, int nele_hess, int *iRow, int *jCol, double *values) override
Method to return: 1) The structure of the hessian of the lagrangian (if "values" is nullptr) 2) The v...
bool eval_grad_f(int n, const double *x, bool new_x, double *grad_f) override
Method to return the gradient of the objective
bool eval_jac_g(int n, const double *x, bool new_x, int m, int nele_jac, int *iRow, int *jCol, double *values) override
Method to return: 1) The structure of the jacobian (if "values" is nullptr) 2) The values of the jaco...
void finalize_solution(Ipopt::SolverReturn status, int n, const double *x, const double *z_L, const double *z_U, int m, const double *g, const double *lambda, double obj_value, const Ipopt::IpoptData *ip_data, Ipopt::IpoptCalculatedQuantities *ip_cq) override
This method is called when the algorithm is complete so the TNLP can store/write the solution
Planning module main class.
Math-related util functions.
Some util functions.
class register implement
Definition arena_queue.h:37
optional VehicleParam vehicle_param