controller.cpp

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/*********************************************************************
 *
 *  Software License Agreement
 *
 *  Copyright (c) 2020, Christoph Rösmann, All rights reserved.
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program.  If not, see <https://www.gnu.org/licenses/>.
 *
 *  Authors: Christoph Rösmann
 *********************************************************************/

#include <mpc_local_planner/controller.h>

#include <corbo-optimal-control/functions/hybrid_cost.h>
#include <corbo-optimal-control/functions/minimum_time.h>
#include <corbo-optimal-control/functions/quadratic_control_cost.h>
#include <corbo-optimal-control/structured_ocp/discretization_grids/finite_differences_variable_grid.h>
#include <corbo-optimal-control/structured_ocp/structured_optimal_control_problem.h>
#include <corbo-optimization/hyper_graph/hyper_graph_optimization_problem_edge_based.h>
#include <corbo-optimization/solver/levenberg_marquardt_sparse.h>
#include <corbo-optimization/solver/nlp_solver_ipopt.h>
#include <corbo-optimization/solver/qp_solver_osqp.h>
#include <mpc_local_planner/optimal_control/fd_collocation_se2.h>
#include <mpc_local_planner/optimal_control/final_state_conditions_se2.h>
#include <mpc_local_planner/optimal_control/finite_differences_variable_grid_se2.h>
#include <mpc_local_planner/optimal_control/min_time_via_points_cost.h>
#include <mpc_local_planner/optimal_control/quadratic_cost_se2.h>
#include <mpc_local_planner/optimal_control/stage_inequality_se2.h>
#include <mpc_local_planner/systems/kinematic_bicycle_model.h>
#include <mpc_local_planner/systems/simple_car.h>
#include <mpc_local_planner/systems/unicycle_robot.h>
#include <mpc_local_planner/utils/time_series_se2.h>

#include <mpc_local_planner_msgs/OptimalControlResult.h>
#include <mpc_local_planner_msgs/StateFeedback.h>

#include <corbo-communication/utilities.h>
#include <corbo-core/console.h>

#include <tf2/utils.h>

#include <memory>
#include <mutex>

namespace mpc_local_planner {

bool Controller::configure(ros::NodeHandle& nh, const teb_local_planner::ObstContainer& obstacles,
                           teb_local_planner::RobotFootprintModelPtr robot_model, const std::vector<teb_local_planner::PoseSE2>& via_points)
{
    _dynamics = configureRobotDynamics(nh);
    if (!_dynamics) return false;  // we may need state and control dimensions to check other parameters

    _grid   = configureGrid(nh);
    _solver = configureSolver(nh);

    _structured_ocp = configureOcp(nh, obstacles, robot_model, via_points);
    _ocp            = _structured_ocp;  // copy pointer also to parent member

    int outer_ocp_iterations = 1;
    nh.param("controller/outer_ocp_iterations", outer_ocp_iterations, outer_ocp_iterations);
    setNumOcpIterations(outer_ocp_iterations);

    // further goal opions
    nh.param("controller/force_reinit_new_goal_dist", _force_reinit_new_goal_dist, _force_reinit_new_goal_dist);
    nh.param("controller/force_reinit_new_goal_angular", _force_reinit_new_goal_angular, _force_reinit_new_goal_angular);

    nh.param("controller/allow_init_with_backward_motion", _guess_backwards_motion, _guess_backwards_motion);
    nh.param("controller/force_reinit_num_steps", _force_reinit_num_steps, _force_reinit_num_steps);

    // custom feedback:
    nh.param("controller/prefer_x_feedback", _prefer_x_feedback, _prefer_x_feedback);
    _x_feedback_sub = nh.subscribe("state_feedback", 1, &Controller::stateFeedbackCallback, this);

    // result publisher:
    _ocp_result_pub = nh.advertise<mpc_local_planner_msgs::OptimalControlResult>("ocp_result", 100);
    nh.param("controller/publish_ocp_results", _publish_ocp_results, _publish_ocp_results);
    nh.param("controller/print_cpu_time", _print_cpu_time, _print_cpu_time);

    setAutoUpdatePreviousControl(false);  // we want to update the previous control value manually

    if (_ocp->initialize())
        ROS_INFO("OCP initialized.");
    else
    {
        ROS_ERROR("OCP initialization failed");
        return false;
    }
    return _grid && _dynamics && _solver && _structured_ocp;
}

bool Controller::step(const Controller::PoseSE2& start, const Controller::PoseSE2& goal, const geometry_msgs::Twist& vel, double dt, ros::Time t,
                      corbo::TimeSeries::Ptr u_seq, corbo::TimeSeries::Ptr x_seq)
{
    std::vector<geometry_msgs::PoseStamped> initial_plan(2);
    start.toPoseMsg(initial_plan.front().pose);
    goal.toPoseMsg(initial_plan.back().pose);
    return step(initial_plan, vel, dt, t, u_seq, x_seq);
}

bool Controller::step(const std::vector<geometry_msgs::PoseStamped>& initial_plan, const geometry_msgs::Twist& vel, double dt, ros::Time t,
                      corbo::TimeSeries::Ptr u_seq, corbo::TimeSeries::Ptr x_seq)
{
    if (!_dynamics || !_grid || !_structured_ocp)
    {
        ROS_ERROR("Controller must be configured before invoking step().");
        return false;
    }
    if (initial_plan.size() < 2)
    {
        ROS_ERROR("Controller::step(): initial plan must contain at least two poses.");
        return false;
    }

    PoseSE2 start(initial_plan.front().pose);
    PoseSE2 goal(initial_plan.back().pose);

    Eigen::VectorXd xf(_dynamics->getStateDimension());
    _dynamics->getSteadyStateFromPoseSE2(goal, xf);

    // retrieve or estimate current state
    Eigen::VectorXd x(_dynamics->getStateDimension());
    // check for new measurements
    bool new_x = false;
    {
        std::lock_guard<std::mutex> lock(_x_feedback_mutex);
        new_x = _recent_x_feedback.size() > 0 && (t - _recent_x_time).toSec() < 2.0 * dt;
        if (new_x) x = _recent_x_feedback;
    }
    if (!new_x && (!_x_ts || _x_ts->isEmpty() || !_x_ts->getValuesInterpolate(dt, x)))  // predict with previous state sequence
    {
        _dynamics->getSteadyStateFromPoseSE2(start, x);  // otherwise initialize steady state
    }
    if (!new_x || !_prefer_x_feedback)
    {
        // Merge state feedback with odometry feedback if desired.
        // Note, some models like unicycle overwrite the full state by odom feedback unless _prefer_x_measurement is set to true.
        _dynamics->mergeStateFeedbackAndOdomFeedback(start, vel, x);
    }

    // now check goal
    if (_force_reinit_num_steps > 0 && _ocp_seq % _force_reinit_num_steps == 0) _grid->clear();
    if (!_grid->isEmpty() && ((goal.position() - _last_goal.position()).norm() > _force_reinit_new_goal_dist ||
                              std::abs(normalize_theta(goal.theta() - _last_goal.theta())) > _force_reinit_new_goal_angular))
    {
        // goal pose diverges a lot from the previous one, so force reinit
        _grid->clear();
    }
    if (_grid->isEmpty())
    {
        // generate custom initialization based on initial_plan
        // check if the goal is behind the start pose (w.r.t. start orientation)
        bool backward = _guess_backwards_motion && (goal.position() - start.position()).dot(start.orientationUnitVec()) < 0;
        generateInitialStateTrajectory(x, xf, initial_plan, backward);
    }
    corbo::Time time(t.toSec());
    _x_seq_init.setTimeFromStart(time);

    corbo::StaticReference xref(xf);  // currently, we only support point-to-point transitions in ros
    corbo::ZeroReference uref(_dynamics->getInputDimension());

    _ocp_successful = PredictiveController::step(x, xref, uref, corbo::Duration(dt), time, u_seq, x_seq, nullptr, nullptr, &_x_seq_init);
    // publish results if desired
    if (_publish_ocp_results) publishOptimalControlResult();  // TODO(roesmann): we could also pass time t from above
    ROS_INFO_STREAM_COND(_print_cpu_time, "Cpu time: " << _statistics.step_time.toSec() * 1000.0 << " ms.");
    ++_ocp_seq;
    _last_goal = goal;
    return _ocp_successful;
}

void Controller::stateFeedbackCallback(const mpc_local_planner_msgs::StateFeedback::ConstPtr& msg)
{
    if (!_dynamics) return;

    if ((int)msg->state.size() != _dynamics->getStateDimension())
    {
        ROS_ERROR_STREAM("stateFeedbackCallback(): state feedback dimension does not match robot state dimension: "
                         << msg->state.size() << " != " << _dynamics->getStateDimension());
        return;
    }

    std::lock_guard<std::mutex> lock(_x_feedback_mutex);
    _recent_x_time     = msg->header.stamp;
    _recent_x_feedback = Eigen::Map<const Eigen::VectorXd>(msg->state.data(), (int)msg->state.size());
}

void Controller::publishOptimalControlResult()
{
    if (!_dynamics) return;
    mpc_local_planner_msgs::OptimalControlResult msg;
    msg.header.stamp           = ros::Time::now();
    msg.header.seq             = static_cast<unsigned int>(_ocp_seq);
    msg.dim_states             = _dynamics->getStateDimension();
    msg.dim_controls           = _dynamics->getInputDimension();
    msg.optimal_solution_found = _ocp_successful;
    msg.cpu_time               = _statistics.step_time.toSec();

    if (_x_ts && !_x_ts->isEmpty())
    {
        msg.time_states = _x_ts->getTime();
        msg.states      = _x_ts->getValues();
    }

    if (_u_ts && !_u_ts->isEmpty())
    {
        msg.time_controls = _u_ts->getTime();
        msg.controls      = _u_ts->getValues();
    }

    _ocp_result_pub.publish(msg);
}

void Controller::reset() { PredictiveController::reset(); }

corbo::DiscretizationGridInterface::Ptr Controller::configureGrid(const ros::NodeHandle& nh)
{
    if (!_dynamics) return {};

    std::string grid_type = "fd_grid";
    nh.param("grid/type", grid_type, grid_type);

    if (grid_type == "fd_grid")
    {
        FiniteDifferencesGridSE2::Ptr grid;

        bool variable_grid = true;
        nh.param("grid/variable_grid/enable", variable_grid, variable_grid);
        if (variable_grid)
        {
            FiniteDifferencesVariableGridSE2::Ptr var_grid = std::make_shared<FiniteDifferencesVariableGridSE2>();

            double min_dt = 0.0;
            nh.param("grid/variable_grid/min_dt", min_dt, min_dt);
            double max_dt = 10.0;
            nh.param("grid/variable_grid/max_dt", max_dt, max_dt);
            var_grid->setDtBounds(min_dt, max_dt);

            bool grid_adaptation = true;
            nh.param("grid/variable_grid/grid_adaptation/enable", grid_adaptation, grid_adaptation);

            if (grid_adaptation)
            {
                int max_grid_size = 50;
                nh.param("grid/variable_grid/grid_adaptation/max_grid_size", max_grid_size, max_grid_size);
                double dt_hyst_ratio = 0.1;
                nh.param("grid/variable_grid/grid_adaptation/dt_hyst_ratio", dt_hyst_ratio, dt_hyst_ratio);
                var_grid->setGridAdaptTimeBasedSingleStep(max_grid_size, dt_hyst_ratio, true);

                int min_grid_size = 2;
                nh.param("grid/variable_grid/grid_adaptation/min_grid_size", min_grid_size, min_grid_size);
                var_grid->setNmin(min_grid_size);
            }
            else
            {
                var_grid->disableGridAdaptation();
            }
            grid = var_grid;
        }
        else
        {
            grid = std::make_shared<FiniteDifferencesGridSE2>();
        }
        // common grid parameters
        int grid_size_ref = 20;
        nh.param("grid/grid_size_ref", grid_size_ref, grid_size_ref);
        grid->setNRef(grid_size_ref);

        double dt_ref = 0.3;
        nh.param("grid/dt_ref", dt_ref, dt_ref);
        grid->setDtRef(dt_ref);

        std::vector<bool> xf_fixed = {true, true, true};
        nh.param("grid/xf_fixed", xf_fixed, xf_fixed);
        if ((int)xf_fixed.size() != _dynamics->getStateDimension())
        {
            ROS_ERROR_STREAM("Array size of `xf_fixed` does not match robot state dimension(): " << xf_fixed.size()
                                                                                                 << " != " << _dynamics->getStateDimension());
            return {};
        }
        Eigen::Matrix<bool, -1, 1> xf_fixed_eigen(xf_fixed.size());  // we cannot use Eigen::Map as vector<bool> does not provide raw access
        for (int i = 0; i < (int)xf_fixed.size(); ++i) xf_fixed_eigen[i] = xf_fixed[i];
        grid->setXfFixed(xf_fixed_eigen);

        bool warm_start = true;
        nh.param("grid/warm_start", warm_start, warm_start);
        grid->setWarmStart(warm_start);

        std::string collocation_method = "forward_differences";
        nh.param("grid/collocation_method", collocation_method, collocation_method);

        if (collocation_method == "forward_differences")
        {
            grid->setFiniteDifferencesCollocationMethod(std::make_shared<ForwardDiffCollocationSE2>());
        }
        else if (collocation_method == "midpoint_differences")
        {
            grid->setFiniteDifferencesCollocationMethod(std::make_shared<MidpointDiffCollocationSE2>());
        }
        else if (collocation_method == "crank_nicolson_differences")
        {
            grid->setFiniteDifferencesCollocationMethod(std::make_shared<CrankNicolsonDiffCollocationSE2>());
        }
        else
        {
            ROS_ERROR_STREAM("Unknown collocation method '" << collocation_method << "' specified. Falling back to default...");
        }

        std::string cost_integration_method = "left_sum";
        nh.param("grid/cost_integration_method", cost_integration_method, cost_integration_method);

        if (cost_integration_method == "left_sum")
        {
            grid->setCostIntegrationRule(FullDiscretizationGridBaseSE2::CostIntegrationRule::LeftSum);
        }
        else if (cost_integration_method == "trapezoidal_rule")
        {
            grid->setCostIntegrationRule(FullDiscretizationGridBaseSE2::CostIntegrationRule::TrapezoidalRule);
        }
        else
        {
            ROS_ERROR_STREAM("Unknown cost integration method '" << cost_integration_method << "' specified. Falling back to default...");
        }

        return std::move(grid);
    }
    else
    {
        ROS_ERROR_STREAM("Unknown grid type '" << grid_type << "' specified.");
    }

    return {};
}

RobotDynamicsInterface::Ptr Controller::configureRobotDynamics(const ros::NodeHandle& nh)
{
    _robot_type = "unicycle";
    nh.param("robot/type", _robot_type, _robot_type);

    if (_robot_type == "unicycle")
    {
        return std::make_shared<UnicycleModel>();
    }
    else if (_robot_type == "simple_car")
    {
        double wheelbase = 0.5;
        nh.param("robot/simple_car/wheelbase", wheelbase, wheelbase);
        bool front_wheel_driving = false;
        nh.param("robot/simple_car/front_wheel_driving", front_wheel_driving, front_wheel_driving);
        if (front_wheel_driving)
            return std::make_shared<SimpleCarFrontWheelDrivingModel>(wheelbase);
        else
            return std::make_shared<SimpleCarModel>(wheelbase);
    }
    else if (_robot_type == "kinematic_bicycle_vel_input")
    {
        double length_rear = 1.0;
        nh.param("robot/kinematic_bicycle_vel_input/length_rear", length_rear, length_rear);
        double length_front = 1.0;
        nh.param("robot/kinematic_bicycle_vel_input/length_front", length_front, length_front);
        return std::make_shared<KinematicBicycleModelVelocityInput>(length_rear, length_front);
    }
    else
    {
        ROS_ERROR_STREAM("Unknown robot type '" << _robot_type << "' specified.");
    }

    return {};
}

corbo::NlpSolverInterface::Ptr Controller::configureSolver(const ros::NodeHandle& nh)
{

    std::string solver_type = "ipopt";
    nh.param("solver/type", solver_type, solver_type);

    if (solver_type == "ipopt")
    {
        corbo::SolverIpopt::Ptr solver = std::make_shared<corbo::SolverIpopt>();
        solver->initialize();  // requried for setting parameters afterward

        int iterations = 100;
        nh.param("solver/ipopt/iterations", iterations, iterations);
        solver->setIterations(iterations);

        double max_cpu_time = -1.0;
        nh.param("solver/ipopt/max_cpu_time", max_cpu_time, max_cpu_time);
        solver->setMaxCpuTime(max_cpu_time);

        // now check for additional ipopt options
        std::map<std::string, double> numeric_options;
        nh.param("solver/ipopt/ipopt_numeric_options", numeric_options, numeric_options);
        for (const auto& item : numeric_options)
        {
            if (!solver->setIpoptOptionNumeric(item.first, item.second)) ROS_WARN_STREAM("Ipopt option " << item.first << " could not be set.");
        }

        std::map<std::string, std::string> string_options;
        nh.param("solver/ipopt/ipopt_string_options", string_options, string_options);
        for (const auto& item : string_options)
        {
            if (!solver->setIpoptOptionString(item.first, item.second)) ROS_WARN_STREAM("Ipopt option " << item.first << " could not be set.");
        }

        std::map<std::string, int> integer_options;
        nh.param("solver/ipopt/ipopt_integer_options", integer_options, integer_options);
        for (const auto& item : integer_options)
        {
            if (!solver->setIpoptOptionInt(item.first, item.second)) ROS_WARN_STREAM("Ipopt option " << item.first << " could not be set.");
        }

        return std::move(solver);
    }
    //    else if (solver_type == "sqp")
    //    {
    //        corbo::SolverSQP::Ptr solver = std::make_shared<corbo::SolverSQP>();
    //        solver->setUseObjectiveHessian(true);
    //        // solver->setQpZeroWarmstart(false);
    //        solver->setVerbose(true);
    //        corbo::SolverOsqp::Ptr qp_solver      = std::make_shared<corbo::SolverOsqp>();
    //        qp_solver->getOsqpSettings()->verbose = 1;
    //        solver->setQpSolver(qp_solver);
    //        corbo::LineSearchL1::Ptr ls = std::make_shared<corbo::LineSearchL1>();
    //        ls->setVerbose(true);
    //        ls->setEta(1e-6);
    //        solver->setLineSearchAlgorithm(ls);

    //        return std::move(solver);
    //    }
    else if (solver_type == "lsq_lm")
    {
        corbo::LevenbergMarquardtSparse::Ptr solver = std::make_shared<corbo::LevenbergMarquardtSparse>();

        int iterations = 10;
        nh.param("solver/lsq_lm/iterations", iterations, iterations);
        solver->setIterations(iterations);

        double weight_init_eq = 2;
        nh.param("solver/lsq_lm/weight_init_eq", weight_init_eq, weight_init_eq);
        double weight_init_ineq = 2;
        nh.param("solver/lsq_lm/weight_init_ineq", weight_init_ineq, weight_init_ineq);
        double weight_init_bounds = 2;
        nh.param("solver/lsq_lm/weight_init_bounds", weight_init_bounds, weight_init_bounds);

        solver->setPenaltyWeights(weight_init_eq, weight_init_ineq, weight_init_bounds);

        double weight_adapt_factor_eq = 1;
        nh.param("solver/lsq_lm/weight_adapt_factor_eq", weight_adapt_factor_eq, weight_adapt_factor_eq);
        double weight_adapt_factor_ineq = 1;
        nh.param("solver/lsq_lm/weight_adapt_factor_ineq", weight_adapt_factor_ineq, weight_adapt_factor_ineq);
        double weight_adapt_factor_bounds = 1;
        nh.param("solver/lsq_lm/weight_adapt_factor_bounds", weight_adapt_factor_bounds, weight_adapt_factor_bounds);

        double weight_adapt_max_eq = 500;
        nh.param("solver/lsq_lm/weight_adapt_max_eq", weight_adapt_max_eq, weight_adapt_max_eq);
        double weight_adapt_max_ineq = 500;
        nh.param("solver/lsq_lm/weight_init_eq", weight_adapt_max_ineq, weight_adapt_max_ineq);
        double weight_adapt_max_bounds = 500;
        nh.param("solver/lsq_lm/weight_adapt_max_bounds", weight_adapt_max_bounds, weight_adapt_max_bounds);

        solver->setWeightAdapation(weight_adapt_factor_eq, weight_adapt_factor_ineq, weight_adapt_factor_bounds, weight_adapt_max_eq,
                                   weight_adapt_max_ineq, weight_adapt_max_bounds);

        return std::move(solver);
    }
    else
    {
        ROS_ERROR_STREAM("Unknown solver type '" << solver_type << "' specified.");
    }

    return {};
}

corbo::StructuredOptimalControlProblem::Ptr Controller::configureOcp(const ros::NodeHandle& nh, const teb_local_planner::ObstContainer& obstacles,
                                                                     teb_local_planner::RobotFootprintModelPtr robot_model,
                                                                     const std::vector<teb_local_planner::PoseSE2>& via_points)
{
    corbo::BaseHyperGraphOptimizationProblem::Ptr hg = std::make_shared<corbo::HyperGraphOptimizationProblemEdgeBased>();

    corbo::StructuredOptimalControlProblem::Ptr ocp = std::make_shared<corbo::StructuredOptimalControlProblem>(_grid, _dynamics, hg, _solver);

    const int x_dim = _dynamics->getStateDimension();
    const int u_dim = _dynamics->getInputDimension();

    if (_robot_type == "unicycle")
    {
        double max_vel_x = 0.4;
        nh.param("robot/unicycle/max_vel_x", max_vel_x, max_vel_x);
        double max_vel_x_backwards = 0.2;
        nh.param("robot/unicycle/max_vel_x_backwards", max_vel_x_backwards, max_vel_x_backwards);
        if (max_vel_x_backwards < 0)
        {
            ROS_WARN("max_vel_x_backwards must be >= 0");
            max_vel_x_backwards *= -1;
        }
        double max_vel_theta = 0.3;
        nh.param("robot/unicycle/max_vel_theta", max_vel_theta, max_vel_theta);

        // ocp->setBounds(Eigen::Vector3d(-corbo::CORBO_INF_DBL, -corbo::CORBO_INF_DBL, -corbo::CORBO_INF_DBL),
        //               Eigen::Vector3d(corbo::CORBO_INF_DBL, corbo::CORBO_INF_DBL, corbo::CORBO_INF_DBL),
        //               Eigen::Vector2d(-max_vel_x_backwards, -max_vel_theta), Eigen::Vector2d(max_vel_x, max_vel_theta));
        ocp->setControlBounds(Eigen::Vector2d(-max_vel_x_backwards, -max_vel_theta), Eigen::Vector2d(max_vel_x, max_vel_theta));
    }
    else if (_robot_type == "simple_car")
    {
        double max_vel_x = 0.4;
        nh.param("robot/simple_car/max_vel_x", max_vel_x, max_vel_x);
        double max_vel_x_backwards = 0.2;
        nh.param("robot/simple_car/max_vel_x_backwards", max_vel_x_backwards, max_vel_x_backwards);
        if (max_vel_x_backwards < 0)
        {
            ROS_WARN("max_vel_x_backwards must be >= 0");
            max_vel_x_backwards *= -1;
        }
        double max_steering_angle = 1.5;
        nh.param("robot/simple_car/max_steering_angle", max_steering_angle, max_steering_angle);

        ocp->setControlBounds(Eigen::Vector2d(-max_vel_x_backwards, -max_steering_angle), Eigen::Vector2d(max_vel_x, max_steering_angle));
    }
    else if (_robot_type == "kinematic_bicycle_vel_input")
    {
        double max_vel_x = 0.4;
        nh.param("robot/kinematic_bicycle_vel_input/max_vel_x", max_vel_x, max_vel_x);
        double max_vel_x_backwards = 0.2;
        nh.param("robot/kinematic_bicycle_vel_input/max_vel_x_backwards", max_vel_x_backwards, max_vel_x_backwards);
        if (max_vel_x_backwards < 0)
        {
            ROS_WARN("max_vel_x_backwards must be >= 0");
            max_vel_x_backwards *= -1;
        }
        double max_steering_angle = 1.5;
        nh.param("robot/kinematic_bicycle_vel_input/max_steering_angle", max_steering_angle, max_steering_angle);

        ocp->setControlBounds(Eigen::Vector2d(-max_vel_x_backwards, -max_steering_angle), Eigen::Vector2d(max_vel_x, max_steering_angle));
    }
    else
    {
        ROS_ERROR_STREAM("Cannot configure OCP for unknown robot type " << _robot_type << ".");
        return {};
    }

    std::string objective_type = "minimum_time";
    nh.param("planning/objective/type", objective_type, objective_type);
    bool lsq_solver = _solver->isLsqSolver();

    if (objective_type == "minimum_time")
    {
        ocp->setStageCost(std::make_shared<corbo::MinimumTime>(lsq_solver));
    }
    else if (objective_type == "quadratic_form")
    {
        std::vector<double> state_weights;
        nh.param("planning/objective/quadratic_form/state_weights", state_weights, state_weights);
        Eigen::MatrixXd Q;
        if (state_weights.size() == x_dim)
        {
            Q = Eigen::Map<Eigen::VectorXd>(state_weights.data(), x_dim).asDiagonal();
        }
        else if (state_weights.size() == x_dim * x_dim)
        {
            Q = Eigen::Map<Eigen::MatrixXd>(state_weights.data(), x_dim, x_dim);  // Eigens default is column major
        }
        else
        {
            ROS_ERROR_STREAM("State weights dimension invalid. Must be either " << x_dim << " x 1 or " << x_dim << " x " << x_dim << ".");
            return {};
        }
        std::vector<double> control_weights;
        nh.param("planning/objective/quadratic_form/control_weights", control_weights, control_weights);
        Eigen::MatrixXd R;
        if (control_weights.size() == u_dim)
        {
            R = Eigen::Map<Eigen::VectorXd>(control_weights.data(), u_dim).asDiagonal();
        }
        else if (control_weights.size() == u_dim * u_dim)
        {
            R = Eigen::Map<Eigen::MatrixXd>(control_weights.data(), u_dim, u_dim);  // Eigens default is column major
        }
        else
        {
            ROS_ERROR_STREAM("Control weights dimension invalid. Must be either " << u_dim << " x 1 or " << u_dim << " x " << u_dim << ".");
            return {};
        }
        bool integral_form = false;
        nh.param("planning/objective/quadratic_form/integral_form", integral_form, integral_form);
        bool hybrid_cost_minimum_time = false;
        nh.param("planning/objective/quadratic_form/hybrid_cost_minimum_time", hybrid_cost_minimum_time, hybrid_cost_minimum_time);

        bool q_zero = Q.isZero();
        bool r_zero = R.isZero();
        if (!q_zero && !r_zero)
        {
            PRINT_ERROR_COND(hybrid_cost_minimum_time,
                             "Hybrid minimum time and quadratic form cost is currently only supported for non-zero control weights only. Falling "
                             "back to quadratic form.");
            ocp->setStageCost(std::make_shared<QuadraticFormCostSE2>(Q, R, integral_form, lsq_solver));
        }
        else if (!q_zero && r_zero)
        {
            PRINT_ERROR_COND(hybrid_cost_minimum_time,
                             "Hybrid minimum time and quadratic form cost is currently only supported for non-zero control weights only. Falling "
                             "back to only quadratic state cost.");
            ocp->setStageCost(std::make_shared<QuadraticStateCostSE2>(Q, integral_form, lsq_solver));
        }
        else if (q_zero && !r_zero)
        {
            if (hybrid_cost_minimum_time)
            {
                ocp->setStageCost(std::make_shared<corbo::MinTimeQuadraticControls>(R, integral_form, lsq_solver));
            }
            else
            {
                ocp->setStageCost(std::make_shared<corbo::QuadraticControlCost>(R, integral_form, lsq_solver));
            }
        }
    }
    else if (objective_type == "minimum_time_via_points")
    {
        bool via_points_ordered = false;
        nh.param("planning/objective/minimum_time_via_points/via_points_ordered", via_points_ordered, via_points_ordered);
        double position_weight = 1.0;
        nh.param("planning/objective/minimum_time_via_points/position_weight", position_weight, position_weight);
        double orientation_weight = 0.0;
        nh.param("planning/objective/minimum_time_via_points/orientation_weight", orientation_weight, orientation_weight);
        ocp->setStageCost(std::make_shared<MinTimeViaPointsCost>(via_points, position_weight, orientation_weight, via_points_ordered));
        // TODO(roesmann): lsq version
    }
    else
    {
        ROS_ERROR_STREAM("Unknown objective type '" << objective_type << "' specified ('planning/objective/type').");
        return {};
    }

    std::string terminal_cost = "none";
    nh.param("planning/terminal_cost/type", terminal_cost, terminal_cost);

    if (terminal_cost == "none")
    {
        ocp->setFinalStageCost({});
    }
    else if (terminal_cost == "quadratic")
    {
        std::vector<double> state_weights;
        nh.param("planning/terminal_cost/quadratic/final_state_weights", state_weights, state_weights);
        Eigen::MatrixXd Qf;
        if (state_weights.size() == x_dim)
        {
            Qf = Eigen::Map<Eigen::VectorXd>(state_weights.data(), x_dim).asDiagonal();
        }
        else if (state_weights.size() == x_dim * x_dim)
        {
            Qf = Eigen::Map<Eigen::MatrixXd>(state_weights.data(), x_dim, x_dim);  // Eigens default is column major
        }
        else
        {
            ROS_ERROR_STREAM("Final state weights dimension invalid. Must be either " << x_dim << " x 1 or " << x_dim << " x " << x_dim << ".");
            return {};
        }
        ocp->setFinalStageCost(std::make_shared<QuadraticFinalStateCostSE2>(Qf, lsq_solver));
    }
    else
    {
        ROS_ERROR_STREAM("Unknown terminal_cost type '" << terminal_cost << "' specified ('planning/terminal_cost/type').");
        return {};
    }

    std::string terminal_constraint = "none";
    nh.param("planning/terminal_constraint/type", terminal_constraint, terminal_constraint);

    if (terminal_constraint == "none")
    {
        ocp->setFinalStageConstraint({});
    }
    else if (terminal_constraint == "l2_ball")
    {
        std::vector<double> weight_matrix;
        nh.param("planning/terminal_constraint/l2_ball/weight_matrix", weight_matrix, weight_matrix);
        Eigen::MatrixXd S;
        if (weight_matrix.size() == x_dim)
        {
            S = Eigen::Map<Eigen::VectorXd>(weight_matrix.data(), x_dim).asDiagonal();
        }
        else if (weight_matrix.size() == x_dim * x_dim)
        {
            S = Eigen::Map<Eigen::MatrixXd>(weight_matrix.data(), x_dim, x_dim);  // Eigens default is column major
        }
        else
        {
            ROS_ERROR_STREAM("l2-ball weight_matrix dimensions invalid. Must be either " << x_dim << " x 1 or " << x_dim << " x " << x_dim << ".");
            return {};
        }
        double radius = 1.0;
        nh.param("planning/terminal_constraint/l2_ball/radius", radius, radius);
        ocp->setFinalStageConstraint(std::make_shared<TerminalBallSE2>(S, radius));
    }
    else
    {
        ROS_ERROR_STREAM("Unknown terminal_constraint type '" << terminal_constraint << "' specified ('planning/terminal_constraint/type').");
        return {};
    }

    _inequality_constraint = std::make_shared<StageInequalitySE2>();
    _inequality_constraint->setObstacleVector(obstacles);
    _inequality_constraint->setRobotFootprintModel(robot_model);

    // configure collision avoidance

    double min_obstacle_dist = 0.5;
    nh.param("collision_avoidance/min_obstacle_dist", min_obstacle_dist, min_obstacle_dist);
    _inequality_constraint->setMinimumDistance(min_obstacle_dist);

    bool enable_dynamic_obstacles = false;
    nh.param("collision_avoidance/enable_dynamic_obstacles", enable_dynamic_obstacles, enable_dynamic_obstacles);
    _inequality_constraint->setEnableDynamicObstacles(enable_dynamic_obstacles);

    double force_inclusion_dist = 0.5;
    nh.param("collision_avoidance/force_inclusion_dist", force_inclusion_dist, force_inclusion_dist);
    double cutoff_dist = 2;
    nh.param("collision_avoidance/cutoff_dist", cutoff_dist, cutoff_dist);
    _inequality_constraint->setObstacleFilterParameters(force_inclusion_dist, cutoff_dist);

    // configure control deviation bounds

    if (_robot_type == "unicycle")
    {
        double acc_lim_x = 0.0;
        nh.param("robot/unicycle/acc_lim_x", acc_lim_x, acc_lim_x);
        double dec_lim_x = 0.0;
        nh.param("robot/unicycle/dec_lim_x", dec_lim_x, dec_lim_x);
        if (dec_lim_x < 0)
        {
            ROS_WARN("dec_lim_x must be >= 0");
            dec_lim_x *= -1;
        }
        double acc_lim_theta = 0.0;
        nh.param("robot/unicycle/acc_lim_theta", acc_lim_theta, acc_lim_theta);

        if (acc_lim_x <= 0) acc_lim_x = corbo::CORBO_INF_DBL;
        if (dec_lim_x <= 0) dec_lim_x = corbo::CORBO_INF_DBL;
        if (acc_lim_theta <= 0) acc_lim_theta = corbo::CORBO_INF_DBL;
        Eigen::Vector2d ud_lb(-dec_lim_x, -acc_lim_theta);
        Eigen::Vector2d ud_ub(acc_lim_x, acc_lim_theta);
        _inequality_constraint->setControlDeviationBounds(ud_lb, ud_ub);
    }
    else if (_robot_type == "simple_car")
    {
        double acc_lim_x = 0.0;
        nh.param("robot/simple_car/acc_lim_x", acc_lim_x, acc_lim_x);
        double dec_lim_x = 0.0;
        nh.param("robot/simple_car/dec_lim_x", dec_lim_x, dec_lim_x);
        if (dec_lim_x < 0)
        {
            ROS_WARN("dec_lim_x must be >= 0");
            dec_lim_x *= -1;
        }
        double max_steering_rate = 0.0;
        nh.param("robot/simple_car/max_steering_rate", max_steering_rate, max_steering_rate);

        if (acc_lim_x <= 0) acc_lim_x = corbo::CORBO_INF_DBL;
        if (dec_lim_x <= 0) dec_lim_x = corbo::CORBO_INF_DBL;
        if (max_steering_rate <= 0) max_steering_rate = corbo::CORBO_INF_DBL;
        Eigen::Vector2d ud_lb(-dec_lim_x, -max_steering_rate);
        Eigen::Vector2d ud_ub(acc_lim_x, max_steering_rate);
        _inequality_constraint->setControlDeviationBounds(ud_lb, ud_ub);
    }
    else if (_robot_type == "kinematic_bicycle_vel_input")
    {
        double acc_lim_x = 0.0;
        nh.param("robot/kinematic_bicycle_vel_input/acc_lim_x", acc_lim_x, acc_lim_x);
        double dec_lim_x = 0.0;
        nh.param("robot/kinematic_bicycle_vel_input/dec_lim_x", dec_lim_x, dec_lim_x);
        if (dec_lim_x < 0)
        {
            ROS_WARN("dec_lim_x must be >= 0");
            dec_lim_x *= -1;
        }
        double max_steering_rate = 0.0;
        nh.param("robot/kinematic_bicycle_vel_input/max_steering_rate", max_steering_rate, max_steering_rate);

        if (acc_lim_x <= 0) acc_lim_x = corbo::CORBO_INF_DBL;
        if (dec_lim_x <= 0) dec_lim_x = corbo::CORBO_INF_DBL;
        if (max_steering_rate <= 0) max_steering_rate = corbo::CORBO_INF_DBL;
        Eigen::Vector2d ud_lb(-dec_lim_x, -max_steering_rate);
        Eigen::Vector2d ud_ub(acc_lim_x, max_steering_rate);
        _inequality_constraint->setControlDeviationBounds(ud_lb, ud_ub);
    }
    else
    {
        ROS_ERROR_STREAM("Cannot configure control deviation bounds for unknown robot type " << _robot_type << ".");
        return {};
    }

    ocp->setStageInequalityConstraint(_inequality_constraint);

    return ocp;
}

bool Controller::generateInitialStateTrajectory(const Eigen::VectorXd& x0, const Eigen::VectorXd& xf,
                                                const std::vector<geometry_msgs::PoseStamped>& initial_plan, bool backward)
{
    if (initial_plan.size() < 2 || !_dynamics) return false;

    TimeSeriesSE2::Ptr ts = std::make_shared<TimeSeriesSE2>();

    int n_init = (int)initial_plan.size();
    int n_ref  = _grid->getInitialN();
    if (n_ref < 2)
    {
        ROS_ERROR("Controller::generateInitialStateTrajectory(): grid not properly initialized");
        return false;
    }
    ts->add(0.0, x0);

    double dt_ref = _grid->getInitialDt();
    double tf_ref = (double)(n_ref - 1) * dt_ref;

    Eigen::VectorXd x(_dynamics->getStateDimension());

    // we initialize by assuming equally distributed poses
    double dt_init = tf_ref / double(n_init - 1);

    double t = dt_init;
    for (int i = 1; i < n_init - 1; ++i)
    {
        // get yaw from the orientation of the distance vector between pose_{i+1} and pose_{i}
        double yaw;
        if (_initial_plan_estimate_orientation)
        {
            double dx = initial_plan[i + 1].pose.position.x - initial_plan[i].pose.position.x;
            double dy = initial_plan[i + 1].pose.position.y - initial_plan[i].pose.position.y;
            yaw       = std::atan2(dy, dx);
            if (backward) normalize_theta(yaw + M_PI);
        }
        else
        {
            yaw = tf2::getYaw(initial_plan[i].pose.orientation);
        }
        PoseSE2 intermediate_pose(initial_plan[i].pose.position.x, initial_plan[i].pose.position.y, yaw);
        _dynamics->getSteadyStateFromPoseSE2(intermediate_pose, x);
        ts->add(t, x);
        t += dt_init;
    }

    ts->add(tf_ref, xf);

    _x_seq_init.setTrajectory(ts, corbo::TimeSeries::Interpolation::Linear);
    return true;
}

bool Controller::isPoseTrajectoryFeasible(base_local_planner::CostmapModel* costmap_model, const std::vector<geometry_msgs::Point>& footprint_spec,
                                          double inscribed_radius, double circumscribed_radius, double min_resolution_collision_check_angular,
                                          int look_ahead_idx)
{
    if (!_grid)
    {
        ROS_ERROR("Controller must be configured before invoking step().");
        return false;
    }
    if (_grid->getN() < 2) return false;

    // we currently require a full discretization grid as we want to have fast access to
    // individual states without requiring any new simulation.
    // Alternatively, other grids could be used in combination with method getStateAndControlTimeSeries()
    const FullDiscretizationGridBaseSE2* fd_grid = dynamic_cast<const FullDiscretizationGridBaseSE2*>(_grid.get());
    if (!fd_grid)
    {
        ROS_ERROR("isPoseTrajectoriyFeasible is currently only implemented for fd grids");
        return true;
    }

    if (look_ahead_idx < 0 || look_ahead_idx >= _grid->getN()) look_ahead_idx = _grid->getN() - 1;

    for (int i = 0; i <= look_ahead_idx; ++i)
    {
        if (costmap_model->footprintCost(fd_grid->getState(i)[0], fd_grid->getState(i)[1], fd_grid->getState(i)[2], footprint_spec, inscribed_radius,
                                         circumscribed_radius) == -1)
        {
            return false;
        }
        // Checks if the distance between two poses is higher than the robot radius or the orientation diff is bigger than the specified threshold
        // and interpolates in that case.
        // (if obstacles are pushing two consecutive poses away, the center between two consecutive poses might coincide with the obstacle ;-)!
        if (i < look_ahead_idx)
        {
            double delta_rot           = normalize_theta(fd_grid->getState(i + 1)[2] - fd_grid->getState(i)[2]);
            Eigen::Vector2d delta_dist = fd_grid->getState(i + 1).head(2) - fd_grid->getState(i).head(2);
            if (std::abs(delta_rot) > min_resolution_collision_check_angular || delta_dist.norm() > inscribed_radius)
            {
                int n_additional_samples = std::max(std::ceil(std::abs(delta_rot) / min_resolution_collision_check_angular),
                                                    std::ceil(delta_dist.norm() / inscribed_radius)) -
                                           1;

                PoseSE2 intermediate_pose(fd_grid->getState(i)[0], fd_grid->getState(i)[1], fd_grid->getState(i)[2]);
                for (int step = 0; step < n_additional_samples; ++step)
                {
                    intermediate_pose.position() = intermediate_pose.position() + delta_dist / (n_additional_samples + 1.0);
                    intermediate_pose.theta()    = g2o::normalize_theta(intermediate_pose.theta() + delta_rot / (n_additional_samples + 1.0));
                    if (costmap_model->footprintCost(intermediate_pose.x(), intermediate_pose.y(), intermediate_pose.theta(), footprint_spec,
                                                     inscribed_radius, circumscribed_radius) == -1)
                    {
                        return false;
                    }
                }
            }
        }
    }
    return true;
}

}  // namespace mpc_local_planner