Repository: mlab-upenn/LearningMPC Branch: master Commit: 50fcc219037c Files: 14 Total size: 177.1 KB Directory structure: gitextract_5zinre62/ ├── CMakeLists.txt ├── Lmpc_params.yaml ├── README.md ├── data/ │ ├── centerline_waypoints.csv │ └── initial_safe_set.csv ├── include/ │ └── LearningMPC/ │ ├── CSVReader.h │ ├── car_params.h │ ├── occupancy_grid.h │ ├── spline.h │ └── track.h ├── launch/ │ └── lmpc.launch ├── package.xml └── src/ ├── LMPC.cpp └── record_initial_safe_set.cpp ================================================ FILE CONTENTS ================================================ ================================================ FILE: CMakeLists.txt ================================================ cmake_minimum_required(VERSION 2.8.3) project(LearningMPC) ## Compile as C++11, supported in ROS Kinetic and newer add_compile_options(-std=c++11) ## Find catkin macros and libraries ## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) ## is used, also find other catkin packages find_package(catkin REQUIRED COMPONENTS roscpp rospy std_msgs ) ## System dependencies are found with CMake's conventions # find_package(Boost REQUIRED COMPONENTS system) find_package(Eigen3 REQUIRED) find_package(OsqpEigen REQUIRED) ## System dependencies are found with CMake's conventions find_package(Boost REQUIRED COMPONENTS system) ## Uncomment this if the package has a setup.py. This macro ensures ## modules and global scripts declared therein get installed ## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html # catkin_python_setup() ################################################ ## Declare ROS messages, services and actions ## ################################################ ## To declare and build messages, services or actions from within this ## package, follow these steps: ## * Let MSG_DEP_SET be the set of packages whose message types you use in ## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...). ## * In the file package.xml: ## * add a build_depend tag for "message_generation" ## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET ## * If MSG_DEP_SET isn't empty the following dependency has been pulled in ## but can be declared for certainty nonetheless: ## * add a exec_depend tag for "message_runtime" ## * In this file (CMakeLists.txt): ## * add "message_generation" and every package in MSG_DEP_SET to ## find_package(catkin REQUIRED COMPONENTS ...) ## * add "message_runtime" and every package in MSG_DEP_SET to ## catkin_package(CATKIN_DEPENDS ...) ## * uncomment the add_*_files sections below as needed ## and list every .msg/.srv/.action file to be processed ## * uncomment the generate_messages entry below ## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...) ## Generate messages in the 'msg' folder # add_message_files( # FILES # Message1.msg # Message2.msg # ) ## Generate services in the 'srv' folder # add_service_files( # FILES # Service1.srv # Service2.srv # ) ## Generate actions in the 'action' folder # add_action_files( # FILES # Action1.action # Action2.action # ) ## Generate added messages and services with any dependencies listed here # generate_messages( # DEPENDENCIES # std_msgs # Or other packages containing msgs # ) ################################################ ## Declare ROS dynamic reconfigure parameters ## ################################################ ## To declare and build dynamic reconfigure parameters within this ## package, follow these steps: ## * In the file package.xml: ## * add a build_depend and a exec_depend tag for "dynamic_reconfigure" ## * In this file (CMakeLists.txt): ## * add "dynamic_reconfigure" to ## find_package(catkin REQUIRED COMPONENTS ...) ## * uncomment the "generate_dynamic_reconfigure_options" section below ## and list every .cfg file to be processed ## Generate dynamic reconfigure parameters in the 'cfg' folder # generate_dynamic_reconfigure_options( # cfg/DynReconf1.cfg # cfg/DynReconf2.cfg # ) ################################### ## catkin specific configuration ## ################################### ## The catkin_package macro generates cmake config files for your package ## Declare things to be passed to dependent projects ## INCLUDE_DIRS: uncomment this if your package contains header files ## LIBRARIES: libraries you create in this project that dependent projects also need ## CATKIN_DEPENDS: catkin_packages dependent projects also need ## DEPENDS: system dependencies of this project that dependent projects also need catkin_package( INCLUDE_DIRS include LIBRARIES LearningMPC CATKIN_DEPENDS roscpp rospy DEPENDS system_lib ) ########### ## Build ## ########### ## Specify additional locations of header files ## Your package locations should be listed before other locations include_directories( include ${catkin_INCLUDE_DIRS} ${EIGEN3_INCLUDE_DIRS} ) add_executable(LMPC src/LMPC.cpp) target_link_libraries(LMPC ${catkin_LIBRARIES}) add_dependencies(LMPC ${catkin_EXPORTED_TARGETS}) add_executable(record_init_ss src/record_initial_safe_set.cpp) target_link_libraries(record_init_ss ${catkin_LIBRARIES}) add_dependencies(record_init_ss ${catkin_EXPORTED_TARGETS}) target_link_libraries(LMPC OsqpEigen::OsqpEigen osqp::osqp) ## Declare a C++ library # add_library(${PROJECT_NAME} # src/${PROJECT_NAME}/LearningMPC.cpp # ) ## Add cmake target dependencies of the library ## as an example, code may need to be generated before libraries ## either from message generation or dynamic reconfigure # add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) ## Declare a C++ executable ## With catkin_make all packages are built within a single CMake context ## The recommended prefix ensures that target names across packages don't collide # add_executable(${PROJECT_NAME}_node src/LearningMPC_node.cpp) ## Rename C++ executable without prefix ## The above recommended prefix causes long target names, the following renames the ## target back to the shorter version for ease of user use ## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" # set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") ## Add cmake target dependencies of the executable ## same as for the library above # add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) ## Specify libraries to link a library or executable target against # target_link_libraries(${PROJECT_NAME}_node # ${catkin_LIBRARIES} # ) ############# ## Install ## ############# # all install targets should use catkin DESTINATION variables # See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html ## Mark executable scripts (Python etc.) for installation ## in contrast to setup.py, you can choose the destination # install(PROGRAMS # scripts/my_python_script # DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} # ) ## Mark executables for installation ## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html # install(TARGETS ${PROJECT_NAME}_node # RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} # ) ## Mark libraries for installation ## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html # install(TARGETS ${PROJECT_NAME} # ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} # LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} # RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} #) ## Mark cpp header files for installation # install(DIRECTORY include/${PROJECT_NAME}/ #DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} # FILES_MATCHING PATTERN "*.h" # PATTERN ".svn" EXCLUDE # ) ## Mark other files for installation (e.g. launch and bag files, etc.) # install(FILES # # myfile1 # # myfile2 # DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} # ) ############# ## Testing ## ############# ## Add gtest based cpp test target and link libraries # catkin_add_gtest(${PROJECT_NAME}-test test/test_LearningMPC.cpp) # if(TARGET ${PROJECT_NAME}-test) # target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) # endif() ## Add folders to be run by python nosetests # catkin_add_nosetests(test) ================================================ FILE: Lmpc_params.yaml ================================================ pose_topic : "odom" drive_topic: "nav" wp_file_name: "/home/yuwei/yuwei_ws/src/LearningMPC/data/centerline_waypoints.csv" r_accel : 1.5 r_steer: 18.0 q_s: 3000.0 q_s_terminal: 800 N: 25 Ts: 0.05 K_NEAR: 16 SPEED_MAX: 10.00 STEER_MAX: 0.41 ACCELERATION_MAX: 4.0 DECELERATION_MAX: 5.0 VEL_THRESHOLD: 0.8 MAP_MARGIN: 0.32 WAYPOINT_SPACE: 0.2 wheelbase: 0.3302 # meters friction_coeff: 1.2 # - (complete estimate) height_cg: 0.08255 # m (roughly measured to be 3.25 in) l_cg2rear: 0.17145 # m (decently measured to be 6.75 in) l_cg2front: 0.15875 # m (decently measured to be 6.25 in) C_S_front: 2.3 #.79 # 1/rad ? (estimated weight/4) C_S_rear: 2.3 #.79 # 1/rad ? (estimated weight/4) mass: 3.17 # kg (measured on car 'lidart') moment_inertia: .0398378 # kg m^2 (estimated as a rectangle with width and height of car and evenly distributed mass, then shifted to account for center of mass location) random: 1.0 ================================================ FILE: README.md ================================================ # LearningMPC Learning based Model Predictive Control for online iterative trajectory optimization for F1/10 autonomous racing. The F1/10 hardware used for experiments:

The car is learning to improve its racing policy iteratively online as it completes more and more laps, until the policy converges to a minimum lap time. Initial Sample Safe Set is collected using a path following controller. Starting from Lap 3, control policies are generated by learning MPC. Every time a lap is completed, cost-to-go values are updated for all collected samples. Stability and recursive feasibility of MPC is gauranteed by enforcing the terminal state to land within a convex hull of a selected subset of collected samples from previous laps. The MPC manages to minimize the cost-to-go for the terminal state at each run, and is solved with OSQP solver at 20Hz. The sample safe set continues to grow as more and more data is being collected along the way. The improvement of racing performance over laps is shown here below. The black one is the initial lap with path following controller. The colored ones are the improved trajectories given by LMPC ### Dependencies 1. Install the f1/10 racecar simulator: https://github.com/mlab-upenn/f110-fall2019-skeletons/tree/master/racecar_simulator 2. Install QSQP Solver: https://osqp.org/docs/get_started/sources.html 3. Install the C++ Eigen wrapper for OSQP: https://robotology.github.io/osqp-eigen/doxygen/doc/html/index.html ### How to Run Build everything with ```catkin_make``` from the root directory of your catkin workspace First launch the simulator by entering the command below in terminal: roslaunch racecar_simulator simulator.launch Position the car as shown below (The car must be placed ahead of the starting line) using interactive markers in Rviz Then launch LearningMPC node: roslaunch LearningMPC lmpc.launch Enter ```n``` in the simulator terminal to unlock the car ### Results ![](media/laps_compare.PNG) Lap 5 (Top speed 1.2 m/s) ![](media/lap5.gif) Lap 25 (Top speed 3 m/s) ![](media/lap25.gif) After Lap 60, it converges to an optimal policy (Top speed 6 m/s) with precisely controlled high speed drifting. ![](media/lap60_converged_drifting.gif). With low frictions, it converges to a policy with lower top speed but with more drifting. ![](media/drifting_low_friction.gif). Obstacle Avoidance 1: ![](media/obstacle_avoid.gif). Obstacle Avoidance 2 (low friction): ![](media/obstacle_low_friction.gif). Lap time vs number of iterations completed: ![](media/lap_time_decrease.PNG) Converged optimal policy recorded: ![](media/final_traj.PNG) Currently working on Gaussian Process and local linear regression for system identification to update the dynamic model online. The project is inspired by "Learning How to Autonomously Race a Car: a Predictive Control Approach, Ugo Rosolia and Francesco Borrelli" ================================================ FILE: data/centerline_waypoints.csv ================================================ 0.688,-0.906 1.049,-1.92 1.95,-4.108 2.453,-6.082 2.522,-6.570 2.444,-6.922 2.181,-7.290 1.277,-7.851 -0.698,-8.757 -1.972,-9.200 -4.363,-10.040 -5.665,-10.615 -5.852,-10.719 -6.161,-10.994 -6.205,-11.405 -6.009,-12.139 -5.785,-12.619 -5.670,-12.863 -5.376,-13.186 -5.175,-13.232 -4.642,-13.272 -4.085,-13.115 -3.453,-12.924 -2.236,-12.462 -1.260,-12.196 -0.418,-11.838 0.512,-11.379 1.587,-10.849 3.682,-9.708 4.355,-9.275 5.063,-8.669 5.582,-7.996 5.927,-7.119 6.195,-6.046 6.294,-5.217 6.065,-4.417 5.765,-3.476 5.466,-2.774 4.971,-1.823 4.518,-0.906 4.092,-0.210 3.580,0.552 3.327,0.908 3.034,1.223 2.767,1.363 2.419,1.463 1.684,1.346 1.287,1.248 0.808,0.873 0.515,0.444 0.462,0.183 0.477,-0.212 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================================================ FILE: include/LearningMPC/CSVReader.h ================================================ // // Created by yuwei on 11/11/19. // #include #include #include #include #include #include #include class CSVReader { std::string fileName; std::string delimeter; public: CSVReader(std::string filename, std::string delm = ",") : fileName(filename), delimeter(delm) { } std::vector > getData(); }; std::vector > CSVReader::getData() { std::ifstream file(fileName); std::vector > dataList; std::string line = ""; while (getline(file, line)) { std::vector vec; boost::algorithm::split(vec, line, boost::is_any_of(delimeter)); dataList.push_back(vec); } file.close(); return dataList; } ================================================ FILE: include/LearningMPC/car_params.h ================================================ struct CarParams { double wheelbase; double friction_coeff; double h_cg; // height of car's CG double l_f; // length from CG to front axle double l_r; // length from CG to rear axle double cs_f; // cornering stiffness coeff for front wheels double cs_r; // cornering stiffness coeff for rear wheels double mass; double I_z; // moment of inertia about z axis from CG }; ================================================ FILE: include/LearningMPC/occupancy_grid.h ================================================ // // Author: Yuwei Wang #include #include #include #define THRESHOLD 50 using namespace std; namespace occupancy_grid{ int xy_ind2ind(const nav_msgs::OccupancyGrid& grid, int x_ind, int y_ind){ return max(0, min(y_ind * int(grid.info.width) + x_ind, int(grid.data.size())-1)); } int xy2ind(const nav_msgs::OccupancyGrid& grid, float x, float y){ int x_ind = static_cast(ceil((x-grid.info.origin.position.x)/grid.info.resolution))-1; int y_ind = static_cast(ceil((y-grid.info.origin.position.y)/grid.info.resolution))-1; return xy_ind2ind(grid, x_ind, y_ind); } struct Pair{ int x_ind; int y_ind; }; Pair ind2xy_ind(const nav_msgs::OccupancyGrid& grid, int ind){ int y_ind = ind/grid.info.width; int x_ind = ind - y_ind*grid.info.width; Pair res; res.x_ind = x_ind; res.y_ind = y_ind; return res; } float ind2x(const nav_msgs::OccupancyGrid& grid, int ind){ return grid.info.origin.position.x + ind2xy_ind(grid, ind).x_ind * grid.info.resolution; } float ind2y(const nav_msgs::OccupancyGrid& grid, int ind){ return grid.info.origin.position.y + ind2xy_ind(grid, ind).y_ind * grid.info.resolution; } bool is_xy_occupied(nav_msgs::OccupancyGrid& grid, float x, float y){ return int(grid.data.at(xy2ind(grid, x, y))) > THRESHOLD; } void set_xy_occupied(nav_msgs::OccupancyGrid& grid, float x, float y){ grid.data.at(xy2ind(grid, x, y)) = 100; } void inflate_cell(nav_msgs::OccupancyGrid &grid, int i, float margin, int val) { int margin_cells = static_cast(ceil(margin/grid.info.resolution)); Pair res = ind2xy_ind(grid, i); for (int x = max(0, res.x_ind-margin_cells); x occupied_ind; occupied_ind.clear(); for (int i=0; iTHRESHOLD){ occupied_ind.push_back(i); } } for (int i=0; i. * --------------------------------------------------------------------- * */ #ifndef TK_SPLINE_H #define TK_SPLINE_H #include #include #include #include // unnamed namespace only because the implementation is in this // header file and we don't want to export symbols to the obj files namespace { namespace tk { // band matrix solver class band_matrix { private: std::vector< std::vector > m_upper; // upper band std::vector< std::vector > m_lower; // lower band public: band_matrix() {}; // constructor band_matrix(int dim, int n_u, int n_l); // constructor ~band_matrix() {}; // destructor void resize(int dim, int n_u, int n_l); // init with dim,n_u,n_l int dim() const; // matrix dimension int num_upper() const { return m_upper.size()-1; } int num_lower() const { return m_lower.size()-1; } // access operator double & operator () (int i, int j); // write double operator () (int i, int j) const; // read // we can store an additional diogonal (in m_lower) double& saved_diag(int i); double saved_diag(int i) const; void lu_decompose(); std::vector r_solve(const std::vector& b) const; std::vector l_solve(const std::vector& b) const; std::vector lu_solve(const std::vector& b, bool is_lu_decomposed=false); }; // spline interpolation class spline { public: enum bd_type { first_deriv = 1, second_deriv = 2 }; private: std::vector m_x,m_y; // x,y coordinates of points // interpolation parameters // f(x) = a*(x-x_i)^3 + b*(x-x_i)^2 + c*(x-x_i) + y_i std::vector m_a,m_b,m_c; // spline coefficients double m_b0, m_c0; // for left extrapol bd_type m_left, m_right; double m_left_value, m_right_value; bool m_force_linear_extrapolation; public: // set default boundary condition to be zero curvature at both ends spline(): m_left(second_deriv), m_right(second_deriv), m_left_value(0.0), m_right_value(0.0), m_force_linear_extrapolation(false) { ; } // optional, but if called it has to come be before set_points() void set_boundary(bd_type left, double left_value, bd_type right, double right_value, bool force_linear_extrapolation=false); void set_points(const std::vector& x, const std::vector& y, bool cubic_spline=true); double operator() (double x) const; double eval_d(double x); double eval_dd(double x); }; // --------------------------------------------------------------------- // implementation part, which could be separated into a cpp file // --------------------------------------------------------------------- // band_matrix implementation // ------------------------- band_matrix::band_matrix(int dim, int n_u, int n_l) { resize(dim, n_u, n_l); } void band_matrix::resize(int dim, int n_u, int n_l) { assert(dim>0); assert(n_u>=0); assert(n_l>=0); m_upper.resize(n_u+1); m_lower.resize(n_l+1); for(size_t i=0; i0) { return m_upper[0].size(); } else { return 0; } } // defines the new operator (), so that we can access the elements // by A(i,j), index going from i=0,...,dim()-1 double & band_matrix::operator () (int i, int j) { int k=j-i; // what band is the entry assert( (i>=0) && (i=0) && (j diogonal, k<0 lower left part, k>0 upper right part if(k>=0) return m_upper[k][i]; else return m_lower[-k][i]; } double band_matrix::operator () (int i, int j) const { int k=j-i; // what band is the entry assert( (i>=0) && (i=0) && (j diogonal, k<0 lower left part, k>0 upper right part if(k>=0) return m_upper[k][i]; else return m_lower[-k][i]; } // second diag (used in LU decomposition), saved in m_lower double band_matrix::saved_diag(int i) const { assert( (i>=0) && (i=0) && (idim(); i++) { assert(this->operator()(i,i)!=0.0); this->saved_diag(i)=1.0/this->operator()(i,i); j_min=std::max(0,i-this->num_lower()); j_max=std::min(this->dim()-1,i+this->num_upper()); for(int j=j_min; j<=j_max; j++) { this->operator()(i,j) *= this->saved_diag(i); } this->operator()(i,i)=1.0; // prevents rounding errors } // Gauss LR-Decomposition for(int k=0; kdim(); k++) { i_max=std::min(this->dim()-1,k+this->num_lower()); // num_lower not a mistake! for(int i=k+1; i<=i_max; i++) { assert(this->operator()(k,k)!=0.0); x=-this->operator()(i,k)/this->operator()(k,k); this->operator()(i,k)=-x; // assembly part of L j_max=std::min(this->dim()-1,k+this->num_upper()); for(int j=k+1; j<=j_max; j++) { // assembly part of R this->operator()(i,j)=this->operator()(i,j)+x*this->operator()(k,j); } } } } // solves Ly=b std::vector band_matrix::l_solve(const std::vector& b) const { assert( this->dim()==(int)b.size() ); std::vector x(this->dim()); int j_start; double sum; for(int i=0; idim(); i++) { sum=0; j_start=std::max(0,i-this->num_lower()); for(int j=j_start; joperator()(i,j)*x[j]; x[i]=(b[i]*this->saved_diag(i)) - sum; } return x; } // solves Rx=y std::vector band_matrix::r_solve(const std::vector& b) const { assert( this->dim()==(int)b.size() ); std::vector x(this->dim()); int j_stop; double sum; for(int i=this->dim()-1; i>=0; i--) { sum=0; j_stop=std::min(this->dim()-1,i+this->num_upper()); for(int j=i+1; j<=j_stop; j++) sum += this->operator()(i,j)*x[j]; x[i]=( b[i] - sum ) / this->operator()(i,i); } return x; } std::vector band_matrix::lu_solve(const std::vector& b, bool is_lu_decomposed) { assert( this->dim()==(int)b.size() ); std::vector x,y; if(is_lu_decomposed==false) { this->lu_decompose(); } y=this->l_solve(b); x=this->r_solve(y); return x; } // spline implementation // ----------------------- void spline::set_boundary(spline::bd_type left, double left_value, spline::bd_type right, double right_value, bool force_linear_extrapolation) { assert(m_x.size()==0); // set_points() must not have happened yet m_left=left; m_right=right; m_left_value=left_value; m_right_value=right_value; m_force_linear_extrapolation=force_linear_extrapolation; } void spline::set_points(const std::vector& x, const std::vector& y, bool cubic_spline) { assert(x.size()==y.size()); assert(x.size()>2); m_x=x; m_y=y; int n=x.size(); // TODO: maybe sort x and y, rather than returning an error for(int i=0; i rhs(n); for(int i=1; i::const_iterator it; it=std::lower_bound(m_x.begin(),m_x.end(),x); int idx=std::max( int(it-m_x.begin())-1, 0); double h=x-m_x[idx]; double interpol; if(xm_x[n-1]) { // extrapolation to the right interpol=(m_b[n-1]*h + m_c[n-1])*h + m_y[n-1]; } else { // interpolation interpol=((m_a[idx]*h + m_b[idx])*h + m_c[idx])*h + m_y[idx]; } return interpol; } double spline::eval_d(double x){ size_t n=m_x.size(); // find the closest point m_x[idx] < x, idx=0 even if x::const_iterator it; it=std::lower_bound(m_x.begin(),m_x.end(),x); int idx=std::max( int(it-m_x.begin())-1, 0); double h=x-m_x[idx]; double interpol; if(xm_x[n-1]) { // extrapolation to the right interpol=2*m_b[n-1]*h + m_c[n-1]; } else { // interpolation interpol=3*m_a[idx]* h*h + 2*m_b[idx]*h + m_c[idx]; } return interpol; } double spline::eval_dd(double x){ size_t n=m_x.size(); // find the closest point m_x[idx] < x, idx=0 even if x::const_iterator it; it=std::lower_bound(m_x.begin(),m_x.end(),x); int idx=std::max( int(it-m_x.begin())-1, 0); double h=x-m_x[idx]; double interpol; if(xm_x[n-1]) { // extrapolation to the right interpol=2*m_b[n-1]; } else { // interpolation interpol=6*m_a[idx]* h + 2*m_b[idx]; } return interpol; } } // namespace tk } // namespace #endif /* TK_SPLINE_H */ ================================================ FILE: include/LearningMPC/track.h ================================================ // // Created by yuwei on 11/20/19. // #include #include #include #include #include #include #include #include #include #include // standard #include #include #include #include #include #include #include #include #include #include #include #include #include #include const int SEARCH_RANGE = 10; const double HALF_WIDTH_MAX = 0.8; using namespace std; typedef struct Point_ref{ double x; double y; double theta; // theta represents progress along centerline_points double left_half_width; double right_half_width; }Point_ref; class Track{ public: vector centerline_points; tk::spline X_spline; tk::spline Y_spline; double length; double space; nav_msgs::OccupancyGrid map; vector width_info; Track(string file_name, nav_msgs::OccupancyGrid& map, bool sparse=false) : space(0.05), map(map){ space = 0.05; centerline_points.clear(); vector waypoints; CSVReader reader(file_name); // Get the data from CSV File std::vector > dataList = reader.getData(); // Print the content of row by row on screen for(std::vector vec : dataList){ geometry_msgs::Point wp; wp.x = std::stof(vec.at(0)); wp.y = std::stof(vec.at(1)); waypoints.push_back(wp); } /*** process raw waypoints data, extract equally spaced points ***/ int curr = 0; int next =1; Point_ref p_start; p_start.x = waypoints.at(0).x; p_start.y = waypoints.at(0).y; p_start.theta = 0.0; centerline_points.push_back(p_start); float theta = 0.0; while(next < waypoints.size()){ float dist = sqrt(pow(waypoints.at(next).x-waypoints.at(curr).x, 2) +pow(waypoints.at(next).y-waypoints.at(curr).y, 2)); float dist_to_start = sqrt(pow(waypoints.at(next).x-waypoints.at(0).x, 2) +pow(waypoints.at(next).y-waypoints.at(0).y, 2)); if (dist>space){ theta += dist; Point_ref p; p.x = waypoints.at(next).x; p.y = waypoints.at(next).y; p.theta = theta; p.left_half_width = p.right_half_width = HALF_WIDTH_MAX; centerline_points.push_back(p); curr = next; } next++; // terminate when finished a lap if (next > waypoints.size()/2 && dist_to_start space) { centerline_points.push_back(p_second_last); } centerline_points.push_back(p_last); //close the loop vector X; vector Y; vector thetas; for (int i=0; icenterline_points.size()-1){ i=0;} // if (i<0){ i=centerline_points.size()-1;} // double dist2 = pow(x-centerline_points.at(i).x, 2) + pow(y-centerline_points.at(i).y, 2); // if( dist2 < min_dist2){ // min_dist2 = dist2; // min_ind = i; // } // } // if (sqrt(min_dist2)>HALF_WIDTH_MAX && !global_search){ // return findTheta(x, y, theta_guess, true); // } // Eigen::Vector2d p, p0, p1; // int min_ind_prev = min_ind-1; // int min_ind_next = min_ind+1; // if (min_ind_next>centerline_points.size()-1){ min_ind_next -= centerline_points.size();} // if (min_ind_prev<0){ min_ind_prev += centerline_points.size();} // // //closest line segment: either [min_ind ,min_ind+1] or [min_ind,min_ind-1] // if (pow(x-centerline_points.at(min_ind_next).x, 2) + pow(y-centerline_points.at(min_ind_next).y, 2) < // pow(x-centerline_points.at(min_ind_prev).x, 2) + pow(y-centerline_points.at(min_ind_prev).y, 2)){ // second_min_ind = min_ind_next; // } // else{ // second_min_ind = min_ind_prev; // } // // p(0) = x; p(1) = y; // p0(0) = centerline_points.at(min_ind).x; p0(1) = centerline_points.at(min_ind).y; // p1(0) = centerline_points.at(second_min_ind).x; p1(1) = centerline_points.at(second_min_ind).y; // // double projection = abs((p - p0).dot(p1 - p0)/(p1 - p0).norm()); // double theta; // // if (min_ind > second_min_ind){ // theta = centerline_points.at(min_ind).theta - projection; // } // else { // if (min_ind == 0 && second_min_ind == centerline_points.size()-1) { // theta = length - projection; // } else { // theta = centerline_points.at(min_ind).theta + projection; // } // } // if (theta>length){ theta -= length;} // if (theta<0){ theta += length;} int min_ind; double min_dist2 = 10000000.0; for (int i=0; i length){ theta -= length;} while(theta < 0){theta += length; } } double x_eval(double theta){ wrapTheta(theta); return X_spline(theta); } double y_eval(double theta){ wrapTheta(theta); return Y_spline(theta); } double x_eval_d(double theta){ wrapTheta(theta); return X_spline.eval_d(theta); } double y_eval_d(double theta){ wrapTheta(theta); return Y_spline.eval_d(theta); } double x_eval_dd(double theta){ wrapTheta(theta); return X_spline.eval_dd(theta); } double y_eval_dd(double theta){ wrapTheta(theta); return Y_spline.eval_dd(theta); } double getPhi(double theta){ wrapTheta(theta); double dx_dtheta = X_spline.eval_d(theta); double dy_dtheta = Y_spline.eval_d(theta); return atan2(dy_dtheta, dx_dtheta); } double getLeftHalfWidth(double theta){ // wrapTheta(theta); int ind = static_cast(floor(theta/space)); ind = max(0, min(int(centerline_points.size()-1), ind)); return centerline_points.at(ind).left_half_width; } double getRightHalfWidth(double theta){ // wrapTheta(theta); int ind = static_cast(floor(theta/space)); ind = max(0, min(int(centerline_points.size()-1), ind)); return centerline_points.at(ind).right_half_width; } void setHalfWidth(double theta, double left_val, double right_val){ // wrapTheta(theta); int ind = static_cast(floor(theta/space)); ind = max(0, min(int(centerline_points.size()-1), ind)); centerline_points.at(ind).left_half_width = left_val; centerline_points.at(ind).right_half_width = right_val; } double getcenterline_pointsCurvature(double theta){ return (X_spline.eval_d(theta)*Y_spline.eval_dd(theta) - Y_spline.eval_d(theta)*X_spline.eval_dd(theta))/ pow((pow(X_spline.eval_d(theta),2) + pow(Y_spline.eval_d(theta),2)), 1.5); } double getcenterline_pointsRadius(double theta){ return 1.0/(getcenterline_pointsCurvature(theta)); } }; ================================================ FILE: launch/lmpc.launch ================================================ ================================================ FILE: package.xml ================================================ LearningMPC 0.0.0 The LearningMPC package yuwei TODO catkin roscpp rospy roscpp rospy roscpp rospy ================================================ FILE: src/LMPC.cpp ================================================ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "OsqpEigen/OsqpEigen.h" #include #include const int nx = 6; const int nu = 2; const int RRT_INTERVAL = 0.1; using namespace std; using namespace Eigen; struct Sample{ Matrix x; Matrix u; double s; int time; int iter; int cost; }; enum rviz_id{ CENTERLINE, CENTERLINE_POINTS, CENTERLINE_SPLINE, PREDICTION, BORDERLINES, SAFE_SET, TERMINAL_CANDIDATE, DEBUG }; class LMPC{ public: LMPC(ros::NodeHandle& nh); void run(); private: ros::NodeHandle nh_; ros::Publisher track_viz_pub_; ros::Publisher LMPC_viz_pub_; ros::Publisher drive_pub_; ros::Publisher debugger_pub_; ros::Subscriber odom_sub_; ros::Subscriber rrt_sub_; ros::Subscriber map_sub_; /*Paramaters*/ CarParams car; string pose_topic; string drive_topic; string waypoint_file; string init_data_file; double WAYPOINT_SPACE; double Ts; int N; int K_NEAR; double SPEED_MAX; double STEER_MAX; double ACCELERATION_MAX; double DECELERATION_MAX; double MAP_MARGIN; double VEL_THRESHOLD; // MPC params double q_s; double q_s_terminal; double r_accel; double r_steer; Matrix R; Track* track_; //odometry tf::Transform tf_; tf::Vector3 car_pos_; double yaw_; double vel_; double yawdot_; double slip_angle_; double s_prev_; double s_curr_; // use dynamic model or not bool use_dyn_; //Sample Safe set vector> SS_; vector curr_trajectory_; int iter_; int time_; Matrix terminal_state_pred_; // map info nav_msgs::OccupancyGrid map_; nav_msgs::OccupancyGrid map_updated_; vector rrt_path_; VectorXd QPSolution_; bool first_run_; vector border_lines_; void getParameters(ros::NodeHandle& nh); void init_occupancy_grid(); void init_SS_from_data(const string data_file); void map_callback(const nav_msgs::OccupancyGrid::Ptr &map_msg); void rrt_path_callback(const visualization_msgs::Marker::ConstPtr &path_msg); void visualize_centerline(); int reset_QPSolution(int iter); void odom_callback(const nav_msgs::Odometry::ConstPtr &odom_msg); void add_point(); void solve_MPC(const Matrix& terminal_candidate); void get_linearized_dynamics(Matrix& Ad, Matrix& Bd, Matrix& hd, Matrix& x_op, Matrix& u_op, bool use_dyn); Vector3d global_to_track(double x, double y, double yaw, double s); Vector3d track_to_global(double e_y, double e_yaw, double s); void applyControl(); void visualize_mpc_solution(const vector& convex_safe_set, const Matrix& terminal_candidate); Matrix select_terminal_candidate(); void select_convex_safe_set(vector& convex_safe_set, int iter_start, int iter_end, double s); int find_nearest_point(vector& trajectory, double s); void update_cost_to_go(vector& trajectory); }; LMPC::LMPC(ros::NodeHandle &nh): nh_(nh){ getParameters(nh_); init_occupancy_grid(); track_ = new Track(waypoint_file, map_, true); odom_sub_ = nh_.subscribe(pose_topic, 10, &LMPC::odom_callback, this); drive_pub_ = nh_.advertise(drive_topic, 1); rrt_sub_ = nh_.subscribe("path_found", 1, &LMPC::rrt_path_callback, this); map_sub_ = nh_.subscribe("map_updated", 1, &LMPC::map_callback, this); track_viz_pub_ = nh_.advertise("track_centerline", 1); LMPC_viz_pub_ = nh_.advertise("LMPC", 1); debugger_pub_ = nh_.advertise("Debugger", 1); nav_msgs::Odometry odom_msg; boost::shared_ptr odom_ptr; odom_ptr = ros::topic::waitForMessage("odom", ros::Duration(5)); if (odom_ptr == nullptr){cout<< "fail to receive odom message!"<findTheta(x, y,0,true); car_pos_ = tf::Vector3(x, y, 0.0); yaw_ = tf::getYaw(odom_msg.pose.pose.orientation); vel_ = odom_msg.twist.twist.linear.x; yawdot_ = 0; slip_angle_ = 0; iter_ = 2; use_dyn_ = false; init_SS_from_data(init_data_file); } void LMPC::getParameters(ros::NodeHandle &nh) { nh.getParam("pose_topic", pose_topic); nh.getParam("drive_topic", drive_topic); nh.getParam("waypoint_file", waypoint_file); nh.getParam("init_data_file", init_data_file); nh.getParam("N",N); nh.getParam("Ts",Ts); nh.getParam("K_NEAR", K_NEAR); nh.getParam("ACCELERATION_MAX", ACCELERATION_MAX); nh.getParam("DECELERATION_MAX", DECELERATION_MAX); nh.getParam("SPEED_MAX", SPEED_MAX); nh.getParam("STEER_MAX", STEER_MAX); nh.getParam("VEL_THRESHOLD", VEL_THRESHOLD); nh.getParam("WAYPOINT_SPACE", WAYPOINT_SPACE); nh.getParam("r_accel",r_accel); nh.getParam("r_steer",r_steer); nh.getParam("q_s",q_s); nh.getParam("q_s_terminal", q_s_terminal); R.setZero(); R.diagonal() << r_accel, r_steer; nh.getParam("MAP_MARGIN",MAP_MARGIN); nh.getParam("wheelbase", car.wheelbase); nh.getParam("friction_coeff", car.friction_coeff); nh.getParam("height_cg", car.h_cg); nh.getParam("l_cg2rear", car.l_r); nh.getParam("l_cg2front", car.l_f); nh.getParam("C_S_front", car.cs_f); nh.getParam("C_S_rear", car.cs_r); nh.getParam("moment_inertia", car.I_z); nh.getParam("mass", car.mass); } int compare_s(Sample& s1, Sample& s2){ return (s1.s< s2.s); } void LMPC::init_occupancy_grid(){ boost::shared_ptr map_ptr; map_ptr = ros::topic::waitForMessage("map", ros::Duration(5.0)); if (map_ptr == nullptr){ROS_INFO("No map received");} else{ map_ = *map_ptr; map_updated_ = map_; ROS_INFO("Map received"); } ROS_INFO("Initializing occupancy grid for map ..."); occupancy_grid::inflate_map(map_, MAP_MARGIN); } void LMPC::init_SS_from_data(string data_file) { CSVReader reader(data_file); // Get the data from CSV File std::vector> dataList = reader.getData(); SS_.clear(); // Print the content of row by row on screen int time_prev=0; int it =0; vector traj; for(std::vector vec : dataList){ Sample sample; sample.time = std::stof(vec.at(0)); // check if it's a new lap if (sample.time - time_prev < 0) { it++; update_cost_to_go(traj); SS_.push_back(traj); traj.clear(); } sample.x(0) = std::stof(vec.at(1)); sample.x(1) = std::stof(vec.at(2)); sample.x(2) = std::stof(vec.at(3)); sample.x(3) = std::stof(vec.at(4)); sample.x(4) = 0; sample.x(5) = 0; sample.u(0) = std::stof(vec.at(5)); sample.u(1) = std::stof(vec.at(6)); sample.s = std::stof(vec.at(7)); sample.iter = it; traj.push_back(sample); time_prev = sample.time; } update_cost_to_go(traj); SS_.push_back(traj); } void LMPC::map_callback(const nav_msgs::OccupancyGrid::Ptr &map_msg){ visualization_msgs::Marker dots; dots.header.frame_id = "map"; dots.id = rviz_id::DEBUG; dots.ns = "debug_points"; dots.type = visualization_msgs::Marker::POINTS; dots.scale.x = dots.scale.y = 0.1; dots.scale.z = 0.1; dots.action = visualization_msgs::Marker::ADD; dots.pose.orientation.w = 1.0; dots.color.b = 1.0; dots.color.g = 0.5; dots.color.a = 1.0; vector path_processed; if(rrt_path_.empty()) return; for (int i=0; i< rrt_path_.size()-1; i++){ path_processed.push_back(rrt_path_[i]); double dist = sqrt(pow(rrt_path_[i+1].x-rrt_path_[i].x, 2) +pow(rrt_path_[i+1].y-rrt_path_[i].y, 2)); if (dist < RRT_INTERVAL) continue; int num = static_cast(ceil(dist/RRT_INTERVAL)); for(int j=1; j< num; j++){ geometry_msgs::Point p; p.x = rrt_path_[i].x + j*((rrt_path_[i+1].x - rrt_path_[i].x)/num); p.y = rrt_path_[i].y + j*((rrt_path_[i+1].y - rrt_path_[i].y)/num); path_processed.push_back(p); } } for (int i=0; ifindTheta(path_processed[i].x, path_processed[i].y, 0, true); Vector2d p_path(path_processed[i].x,path_processed[i].y); Vector2d p_proj(track_->x_eval(theta), track_->y_eval(theta)); Vector2d p1, p2; int t=0; // search one direction until hit obstacle while(true){ double x = (p_path + t*map_msg->info.resolution*(p_path-p_proj).normalized())(0); double y = (p_path + t*map_msg->info.resolution*(p_path-p_proj).normalized())(1); if(occupancy_grid::is_xy_occupied(*map_msg, x, y)){ p1(0) = x; p1(1) = y; geometry_msgs::Point point; point.x = x; point.y = y; dots.points.push_back(point); break; } t++; } t=0; //search the other direction until hit obstacle while(true){ double x = (p_path - t*map_msg->info.resolution*(p_path-p_proj).normalized())(0); double y = (p_path - t*map_msg->info.resolution*(p_path-p_proj).normalized())(1); if(occupancy_grid::is_xy_occupied(*map_msg, x, y)){ p2(0) = x; p2(1) = y; geometry_msgs::Point point; point.x = x; point.y = y; dots.points.push_back(point); break; } t++; } double dx_dtheta = track_->x_eval_d(theta); double dy_dtheta = track_->y_eval_d(theta); double right_width = Vector2d(dy_dtheta, -dx_dtheta).dot(p1-p_proj)>0 ? (p1-p_proj).norm() : -(p1-p_proj).norm(); double left_width = Vector2d(-dy_dtheta, dx_dtheta).dot(p2-p_proj)>0 ? (p2-p_proj).norm() : -(p2-p_proj).norm(); // right_width = -0.15; // left_width = 0.4; cout<<"p1: "<< p1<setHalfWidth(theta, left_width, right_width); } debugger_pub_.publish(dots); } void LMPC:: rrt_path_callback(const visualization_msgs::Marker::ConstPtr &path_msg){ rrt_path_ = path_msg->points; } void LMPC::odom_callback(const nav_msgs::Odometry::ConstPtr &odom_msg){ //visualize_centerline(); /** process pose info **/ float x = odom_msg->pose.pose.position.x; float y = odom_msg->pose.pose.position.y; s_curr_ = track_->findTheta(x, y,0,true); car_pos_ = tf::Vector3(x, y, 0.0); yaw_ = tf::getYaw(odom_msg->pose.pose.orientation); vel_ = sqrt(pow(odom_msg->twist.twist.linear.x,2) + pow(odom_msg->twist.twist.linear.y,2)); yawdot_ = odom_msg->twist.twist.angular.z; slip_angle_ = atan2(odom_msg->twist.twist.linear.y, odom_msg->twist.twist.linear.x); /** STATE MACHINE: check if dynamic model should be used based on current speed **/ if ((!use_dyn_) && (vel_ > VEL_THRESHOLD)){ use_dyn_ = true; } if(use_dyn_ && (vel_< VEL_THRESHOLD*0.5)){ use_dyn_ = false; } if (vel_ > 4.5) { R(0,0) = 1.3 * r_accel; R(1,1) = 1.8 * r_steer; } } void LMPC::run(){ if (first_run_){ // initialize QPSolution_ from initial Sample Safe Set (using the 2nd iteration) reset_QPSolution(1); } /******** LMPC MAIN LOOP starts ********/ /***check if it is new lap***/ if (s_curr_ - s_prev_ < -track_->length/2){ iter_++; update_cost_to_go(curr_trajectory_); //sort(curr_trajectory_.begin(), curr_trajectory_.end(), compare_s); SS_.push_back(curr_trajectory_); curr_trajectory_.clear(); // reset_QPSolution(iter_-1); time_ = 0; } /*** select terminal state candidate and its convex safe set ***/ Matrix terminal_candidate = select_terminal_candidate(); /** solve MPC and record current state***/ for (int i=0; i<1; i++){ solve_MPC(terminal_candidate); } applyControl(); add_point(); /*** store info and advance to next time step***/ terminal_state_pred_ = QPSolution_.segment(N*nx); s_prev_ = s_curr_; time_++; first_run_ = false; } void LMPC::visualize_centerline(){ visualization_msgs::Marker spline_dots; spline_dots.header.stamp = ros::Time::now(); spline_dots.header.frame_id = "map"; spline_dots.id = rviz_id::CENTERLINE_SPLINE; spline_dots.ns = "centerline"; spline_dots.type = visualization_msgs::Marker::LINE_STRIP; spline_dots.scale.x = spline_dots.scale.y = 0.02; spline_dots.scale.z = 0.02; spline_dots.action = visualization_msgs::Marker::ADD; spline_dots.pose.orientation.w = 1.0; spline_dots.color.b = 1.0; spline_dots.color.a = 1.0; // spline_dots.lifetime = ros::Duration(); for (float t=0.0; tlength; t+=0.05){ geometry_msgs::Point p; p.x = track_->x_eval(t); p.y = track_->y_eval(t); spline_dots.points.push_back(p); } visualization_msgs::MarkerArray markers; markers.markers.push_back(spline_dots); track_viz_pub_.publish(markers); } int LMPC::reset_QPSolution(int iter){ QPSolution_ = VectorXd::Zero((N+1)*nx+ N*nu + nx*(N+1) + (2*K_NEAR+1)); for (int i=0; i(i*nx) = SS_[iter][i].x; if (i((N+1)*nx + i*nu) = SS_[iter][i].u; } } Matrix LMPC::select_terminal_candidate(){ if (first_run_){ return SS_.back()[N].x; } else{ return terminal_state_pred_; } } void LMPC::add_point(){ Sample point; point.x << car_pos_.x(), car_pos_.y(), yaw_, vel_, yawdot_, slip_angle_; point.s = s_curr_; point.iter = iter_; point.time = time_; point.u = QPSolution_.segment((N+1)*nx); curr_trajectory_.push_back(point); } void LMPC::select_convex_safe_set(vector& convex_safe_set, int iter_start, int iter_end, double s){ for (int it = iter_start; it<= iter_end; it++){ int nearest_ind = find_nearest_point(SS_[it], s); int start_ind, end_ind; int lap_cost = SS_[it][0].cost; if (K_NEAR%2 != 0 ) { start_ind = nearest_ind - (K_NEAR-1)/2; end_ind = nearest_ind + (K_NEAR-1)/2; } else{ start_ind = nearest_ind - K_NEAR/2 + 1; end_ind = nearest_ind + K_NEAR/2; } vector curr_set; if (end_ind > SS_[it].size()-1){ // front portion of set crossed finishing line for (int ind=start_ind; ind& trajectory, double s){ // binary search to find closest point to a given s int low = 0; int high = trajectory.size()-1; while (low<=high){ int mid = (low + high)/2; if (s == trajectory[mid].s) return mid; if (s < trajectory[mid].s) high = mid-1; else low = mid+1; } return abs(trajectory[low].s-s) < (abs(trajectory[high].s-s))? low : high; } void LMPC::update_cost_to_go(vector& trajectory){ trajectory[trajectory.size()-1].cost = 0; for (int i=trajectory.size()-2; i>=0; i--){ trajectory[i].cost = trajectory[i+1].cost + 1; } } Vector3d LMPC::global_to_track(double x, double y, double yaw, double s){ double x_proj = track_->x_eval(s); double y_proj = track_->y_eval(s); double e_y = sqrt((x-x_proj)*(x-x_proj) + (y-y_proj)*(y-y_proj)); double dx_ds = track_->x_eval_d(s); double dy_ds = track_->y_eval_d(s); e_y = dx_ds*(y-y_proj) - dy_ds*(x-x_proj) >0 ? e_y : -e_y; double e_yaw = yaw - atan2(dy_ds, dx_ds); while(e_yaw > M_PI) e_yaw -= 2*M_PI; while(e_yaw < -M_PI) e_yaw += 2*M_PI; return Vector3d(e_y, e_yaw, s); } Vector3d LMPC::track_to_global(double e_y, double e_yaw, double s){ double dx_ds = track_->x_eval_d(s); double dy_ds = track_->y_eval_d(s); Vector2d proj(track_->x_eval(s), track_->y_eval(s)); Vector2d pos = proj + Vector2d(-dy_ds, dx_ds).normalized()*e_y; double yaw = e_yaw + atan2(dy_ds, dx_ds); return Vector3d(pos(0), pos(1), yaw); } void LMPC::get_linearized_dynamics(Matrix& Ad, Matrix& Bd, Matrix& hd, Matrix& x_op, Matrix& u_op, bool use_dyn){ double yaw = x_op(2); double v = x_op(3); double accel = u_op(0); double steer = u_op(1); double yaw_dot = x_op(4); double slip_angle = x_op(5); VectorXd dynamics(6), h(6); Matrix A, M12; Matrix B; if (!use_dyn) { // Kinematic Model dynamics(0) = v * cos(yaw); dynamics(1) = v * sin(yaw); dynamics(2) = v * tan(steer)/car.wheelbase; dynamics(3) = accel; dynamics(4) = 0; dynamics(5) = 0; A << 0.0, 0.0, -v*sin(yaw), cos(yaw), 0.0, 0.0, 0.0, 0.0, v*cos(yaw), sin(yaw), 0.0, 0.0, 0.0, 0.0, 0.0, tan(steer)/car.wheelbase, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0; B << 0.0, 0.0, 0.0, 0.0, 0.0, v / (cos(steer) * cos(steer) * car.wheelbase), 1.0, 0.0, 0.0, 0.0, 0.0, 0.0; } else{ // Single Track Dynamic Model double g = 9.81; double rear_val = g * car.l_r - accel * car.h_cg; double front_val = g * car.l_f + accel * car.h_cg; dynamics(0) = v * cos(yaw+slip_angle); dynamics(1) = v * sin(yaw+slip_angle); dynamics(2) = yaw_dot; dynamics(3) = accel; dynamics(4) = (car.friction_coeff * car.mass / (car.I_z * car.wheelbase)) * (car.l_f * car.cs_f * steer * (rear_val) + slip_angle * (car.l_r * car.cs_r * (front_val) - car.l_f * car.cs_f * (rear_val)) - (yaw_dot/v) * (pow(car.l_f, 2) * car.cs_f * (rear_val) + pow(car.l_r, 2) * car.cs_r * (front_val))); // yaw_dot dynamics dynamics(5) = (car.friction_coeff / (v * (car.l_r + car.l_f))) * (car.cs_f * steer * rear_val - slip_angle * (car.cs_r * front_val + car.cs_f * rear_val) + (yaw_dot/v) * (car.cs_r * car.l_r * front_val - car.cs_f * car.l_f * rear_val)) - yaw_dot; // slip_angle dynamics double dfyawdot_dv, dfyawdot_dyawdot, dfyawdot_dslip, dfslip_dv, dfslip_dyawdot, dfslip_dslip; double dfyawdot_da, dfyawdot_dsteer, dfslip_da, dfslip_dsteer; dfyawdot_dv = (car.friction_coeff * car.mass / (car.I_z * car.wheelbase)) * (pow(car.l_f, 2) * car.cs_f * (rear_val) + pow(car.l_r, 2) * car.cs_r * (front_val)) * yaw_dot / pow(v, 2); dfyawdot_dyawdot = -(car.friction_coeff * car.mass / (car.I_z * car.wheelbase)) * (pow(car.l_f, 2) * car.cs_f * (rear_val) + pow(car.l_r, 2) * car.cs_r * (front_val))/v; dfyawdot_dslip = (car.friction_coeff * car.mass / (car.I_z * car.wheelbase)) * (car.l_r * car.cs_r * (front_val) - car.l_f * car.cs_f * (rear_val)); dfslip_dv = -(car.friction_coeff / (car.l_r + car.l_f)) * (car.cs_f * steer * rear_val - slip_angle * (car.cs_r * front_val + car.cs_f * rear_val))/pow(v,2) -2*(car.friction_coeff / (car.l_r + car.l_f)) * (car.cs_r * car.l_r * front_val - car.cs_f * car.l_f * rear_val) * yaw_dot/pow(v,3); dfslip_dyawdot = (car.friction_coeff / (pow(v,2) * (car.l_r + car.l_f))) * (car.cs_r * car.l_r * front_val - car.cs_f * car.l_f * rear_val) - 1; dfslip_dslip = -(car.friction_coeff / (v * (car.l_r + car.l_f)))*(car.cs_r * front_val + car.cs_f * rear_val); dfyawdot_da = (car.friction_coeff * car.mass / (car.I_z * car.wheelbase)) *(-car.l_f*car.cs_f*car.h_cg*steer + car.l_r*car.cs_r*car.h_cg*slip_angle + car.l_f*car.cs_f*car.h_cg*slip_angle - (yaw_dot/v)*(-pow(car.l_f,2)*car.cs_f*car.h_cg) + pow(car.l_r,2)*car.cs_r*car.h_cg); dfyawdot_dsteer = (car.friction_coeff * car.mass / (car.I_z * car.wheelbase)) * (car.l_f * car.cs_f * rear_val); dfslip_da = (car.friction_coeff / (v * (car.l_r + car.l_f))) * (-car.cs_f*car.h_cg*steer - (car.cs_r*car.h_cg - car.cs_f*car.h_cg)*slip_angle + (car.cs_r*car.h_cg*car.l_r + car.cs_f*car.h_cg*car.l_f)*(yaw_dot/v)); dfslip_dsteer = (car.friction_coeff / (v * (car.l_r + car.l_f))) * (car.cs_f * rear_val); A << 0.0, 0.0, -v*sin(yaw+slip_angle), cos(yaw+slip_angle), 0.0, -v*sin(yaw+slip_angle), 0.0, 0.0, v*cos(yaw+slip_angle), sin(yaw+slip_angle), 0.0, v*cos(yaw+slip_angle), 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, dfyawdot_dv, dfyawdot_dyawdot, dfyawdot_dslip, 0.0, 0.0, 0.0, dfslip_dv, dfslip_dyawdot, dfslip_dslip; B << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, dfyawdot_da, dfyawdot_dsteer, dfslip_da, dfslip_dsteer; } /** Discretize using Zero-Order Hold **/ Matrix aux, M; aux.setZero(); aux.block(0,0) << A; aux.block(0, nx) << Matrix::Identity(); M = (aux*Ts).exp(); M12 = M.block(0,nx); h = dynamics - (A*x_op + B*u_op); Ad = (A*Ts).exp(); Bd = M12*B; hd = M12*h; } void wrap_angle(double& angle, const double angle_ref){ while(angle - angle_ref > M_PI) {angle -= 2*M_PI;} while(angle - angle_ref < -M_PI) {angle += 2*M_PI;} } void LMPC::solve_MPC(const Matrix& terminal_candidate){ vector terminal_CSS; double s_t = track_->findTheta(terminal_candidate(0), terminal_candidate(1), 0, true); select_convex_safe_set(terminal_CSS, iter_-2, iter_-1, s_t); /** MPC variables: z = [x0, ..., xN, u0, ..., uN-1, s0, ..., sN, lambda0, ....., lambda(2*K_NEAR), s_t1, s_t2, .. s_t6]* * constraints: dynamics, track bounds, input limits, acceleration limit, slack, lambdas, terminal state, sum of lambda's*/ SparseMatrix HessianMatrix((N+1)*nx+ N*nu + (N+1) + (2*K_NEAR) +nx, (N+1)*nx+ N*nu + (N+1)+ (2*K_NEAR) +nx); SparseMatrix constraintMatrix((N+1)*nx+ 2*(N+1) + N*nu + (N+1) + (N+1) + (2*K_NEAR) + 2*nx+1, (N+1)*nx+ N*nu + (N+1)+ (2*K_NEAR) +nx); VectorXd gradient((N+1)*nx+ N*nu + (N+1) + (2*K_NEAR) +nx); VectorXd lower((N+1)*nx+ 2*(N+1) + N*nu + (N+1) + (N+1) + (2*K_NEAR) + 2*nx+1); VectorXd upper((N+1)*nx+ 2*(N+1) + N*nu + (N+1) + (N+1) + (2*K_NEAR) + 2*nx+1); gradient.setZero(); lower.setZero(); upper.setZero(); Matrix x_k_ref; Matrix u_k_ref; Matrix Ad; Matrix Bd; Matrix x0, hd; border_lines_.clear(); if (use_dyn_) x0 <(i*nx); u_k_ref = QPSolution_.segment((N+1)*nx + i*nu); double s_ref = track_->findTheta(x_k_ref(0), x_k_ref(1), 0, true); get_linearized_dynamics(Ad, Bd, hd, x_k_ref, u_k_ref, use_dyn_); /* form Hessian entries*/ // cost does not depend on x0, only 1 to N if (i>0) { HessianMatrix.insert((N+1)*nx + N*nu + i, (N+1)*nx + N*nu + i) = q_s; } if (i((i+1)*nx) = -hd;//-OsqpEigen::INFTY,-OsqpEigen::INFTY,-OsqpEigen::INFTY,-OsqpEigen::INFTY;//-hd; upper.segment((i+1)*nx) = -hd; //OsqpEigen::INFTY, OsqpEigen::INFTY,OsqpEigen::INFTY,OsqpEigen::INFTY;//-hd; } // -I for each x_k+1 for (int row=0; rowx_eval_d(s_ref); double dy_dtheta = track_->y_eval_d(s_ref); constraintMatrix.insert((N+1)*nx+ 2*i, i*nx) = -dy_dtheta; // a*x constraintMatrix.insert((N+1)*nx+ 2*i, i*nx+1) = dx_dtheta; // b*y constraintMatrix.insert((N+1)*nx+ 2*i, (N+1)*nx +N*nu +i) = 1.0; // min(C1,C2) <= a*x + b*y + s_k <= inf constraintMatrix.insert((N+1)*nx+ 2*i+1, i*nx) = -dy_dtheta; // a*x constraintMatrix.insert((N+1)*nx+ 2*i+1, i*nx+1) = dx_dtheta; // b*y constraintMatrix.insert((N+1)*nx+ 2*i+1, (N+1)*nx +N*nu +i) = -1.0; // -inf <= a*x + b*y - s_k <= max(C1,C2) //get upper line and lower line Vector2d left_tangent_p, right_tangent_p, center_p; Vector2d right_line_p1, right_line_p2, left_line_p1, left_line_p2; geometry_msgs::Point r_p1, r_p2, l_p1, l_p2; center_p << track_->x_eval(s_ref), track_->y_eval(s_ref); right_tangent_p = center_p + track_->getRightHalfWidth(s_ref) * Vector2d(dy_dtheta, -dx_dtheta).normalized(); left_tangent_p = center_p + track_->getLeftHalfWidth(s_ref) * Vector2d(-dy_dtheta, dx_dtheta).normalized(); right_line_p1 = right_tangent_p + 0.15*Vector2d(dx_dtheta, dy_dtheta).normalized(); right_line_p2 = right_tangent_p - 0.15*Vector2d(dx_dtheta, dy_dtheta).normalized(); left_line_p1 = left_tangent_p + 0.15*Vector2d(dx_dtheta, dy_dtheta).normalized(); left_line_p2 = left_tangent_p - 0.15*Vector2d(dx_dtheta, dy_dtheta).normalized(); // For visualizing track boundaries r_p1.x = right_line_p1(0); r_p1.y = right_line_p1(1); r_p2.x = right_line_p2(0); r_p2.y = right_line_p2(1); l_p1.x = left_line_p1(0); l_p1.y = left_line_p1(1); l_p2.x = left_line_p2(0); l_p2.y = left_line_p2(1); border_lines_.push_back(r_p1); border_lines_.push_back(r_p2); border_lines_.push_back(l_p1); border_lines_.push_back(l_p2); double C1 = - dy_dtheta*right_tangent_p(0) + dx_dtheta*right_tangent_p(1); double C2 = - dy_dtheta*left_tangent_p(0) + dx_dtheta*left_tangent_p(1); lower((N+1)*nx+ 2*i) = min(C1, C2); upper((N+1)*nx+ 2*i) = OsqpEigen::INFTY; lower((N+1)*nx+ 2*i+1) = -OsqpEigen::INFTY; upper((N+1)*nx+ 2*i+1) = max(C1, C2); // u_min < u < u_max if (i((N+1)*nx+ 2*(N+1) +i*nu) << -DECELERATION_MAX, -STEER_MAX; upper.segment((N+1)*nx+ 2*(N+1) +i*nu) << ACCELERATION_MAX, STEER_MAX; } //max velocity constraintMatrix.insert((N+1)*nx+ 2*(N+1) + N*nu +i, i*nx+3) = 1; lower((N+1)*nx+ 2*(N+1) + N*nu +i) = 0; upper((N+1)*nx+ 2*(N+1) + N*nu +i) = SPEED_MAX; // s_k >= 0 constraintMatrix.insert((N+1)*nx + 2*(N+1) + N*nu + (N+1) + i, (N+1)*nx+N*nu +i) = 1.0; lower((N+1)*nx + 2*(N+1) + N*nu + (N+1) + i) = 0; upper((N+1)*nx + 2*(N+1) + N*nu + (N+1) + i) = OsqpEigen::INFTY; } int numOfConstraintsSoFar = (N+1)*nx + 2*(N+1) + N*nu + (N+1) + (N+1); // lamda's >= 0 for (int i=0; i<2*K_NEAR; i++){ constraintMatrix.insert(numOfConstraintsSoFar + i, (N+1)*nx+ N*nu + (N+1) + i) = 1.0; lower(numOfConstraintsSoFar + i) = 0; upper(numOfConstraintsSoFar + i) = OsqpEigen::INFTY; } numOfConstraintsSoFar += 2*K_NEAR; // terminal state constraints: -s_t <= -x_N+1 + linear_combination(lambda's) <= s_t // 0 <= s_t -x_N+1 + linear_combination(lambda's) <= inf for (int i=0; i<2*K_NEAR; i++){ for (int state_ind=0; state_ind H_t = HessianMatrix.transpose(); SparseMatrix sparse_I((N+1)*nx+ N*nu + (N+1)+ (2*K_NEAR) +nx, (N+1)*nx+ N*nu + (N+1)+ (2*K_NEAR) +nx); sparse_I.setIdentity(); HessianMatrix = 0.5*(HessianMatrix + H_t) + 0.0000001*sparse_I; OsqpEigen::Solver solver; solver.settings()->setWarmStart(true); solver.data()->setNumberOfVariables((N+1)*nx+ N*nu + (N+1)+ 2*K_NEAR +nx); solver.data()->setNumberOfConstraints((N+1)*nx+ 2*(N+1) + N*nu + (N+1) + (N+1) + 2*K_NEAR + 2*nx+1); if (!solver.data()->setHessianMatrix(HessianMatrix)) throw "fail set Hessian"; if (!solver.data()->setGradient(gradient)){throw "fail to set gradient";} if (!solver.data()->setLinearConstraintsMatrix(constraintMatrix)) throw"fail to set constraint matrix"; if (!solver.data()->setLowerBound(lower)){throw "fail to set lower bound";} if (!solver.data()->setUpperBound(upper)){throw "fail to set upper bound";} if(!solver.initSolver()){ cout<< "fail to initialize solver"<& convex_safe_set, const Matrix& terminal_candidate) { visualization_msgs::MarkerArray markers; visualization_msgs::Marker pred_dots; pred_dots.header.stamp = ros::Time::now(); pred_dots.header.frame_id = "map"; pred_dots.id = rviz_id::PREDICTION; pred_dots.ns = "predicted_positions"; pred_dots.type = visualization_msgs::Marker::POINTS; pred_dots.scale.x = pred_dots.scale.y = pred_dots.scale.z = 0.05; pred_dots.action = visualization_msgs::Marker::ADD; pred_dots.pose.orientation.w = 1.0; pred_dots.color.g = 1.0; pred_dots.color.a = 1.0; for (int i=0; i #include #include #include #include #include #include #include using namespace std; class Record_SS{ public: Record_SS(ros::NodeHandle& nh); private: ros::NodeHandle nh_; ros::Subscriber cmd_sub_; ros::Subscriber odom_sub_; double s_prev_; int lap_; int time_; ofstream data_file_; double ros_time_prev_; nav_msgs::Odometry odom_; double acc_cmd_; Track* track_; void cmd_callback(const ackermann_msgs::AckermannDriveStampedConstPtr &cmd_msg); void odom_callback(const nav_msgs::Odometry::ConstPtr &odom_msg); void accel_cmd_callback(const std_msgs::Float32ConstPtr & accel_cmd); }; Record_SS::Record_SS(ros::NodeHandle &nh) : nh_(nh) { cmd_sub_ = nh_.subscribe("nav", 1, &Record_SS::cmd_callback, this); odom_sub_ = nh_.subscribe("odom", 1, &Record_SS::odom_callback, this); string wp_file; double space; nh.getParam("waypoint_file", wp_file); nh.getParam("WAYPOINT_SPACE", space); boost::shared_ptr map_ptr; map_ptr = ros::topic::waitForMessage("map", ros::Duration(5.0)); if (map_ptr == nullptr){ROS_INFO("No map received");} else{ ROS_INFO("Map received"); nav_msgs::OccupancyGrid map = *map_ptr; track_ = new Track(wp_file, map, true); } nav_msgs::Odometry odom_msg; boost::shared_ptr odom_ptr; odom_ptr = ros::topic::waitForMessage("odom", ros::Duration(5)); if (odom_ptr == nullptr){cout<< "fail to receive odom message!"<findTheta(x,y,0,true); ros_time_prev_ = ros::Time::now().toSec(); time_=0; lap_=0; data_file_.open( "initial_safe_set.csv"); } void Record_SS::odom_callback(const nav_msgs::Odometry::ConstPtr &odom_msg){ odom_ = *odom_msg; } void Record_SS::accel_cmd_callback(const std_msgs::Float32ConstPtr & accel_cmd){ acc_cmd_ = accel_cmd->data; } void Record_SS::cmd_callback(const ackermann_msgs::AckermannDriveStampedConstPtr &cmd_msg) { if (cmd_msg != nullptr){ cout<<"recording: "<drive.speed; double steer_cmd = cmd_msg->drive.steering_angle; double yaw = tf::getYaw(odom_.pose.pose.orientation); float x = odom_.pose.pose.position.x; float y = odom_.pose.pose.position.y; double s_curr = track_->findTheta(x,y,0,true); double vel = odom_.twist.twist.linear.x; // check if is a new lap; if (s_curr - s_prev_ < -track_->length/2){ // cout<<"s_curr: "<1){ //initial two laps completed data_file_.close(); delete(track_); ros::shutdown(); } } data_file_ << time_ <<","<