feat: refact param namespace
parent
977eeebf89
commit
26f54ab8a6
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@ -59,9 +59,9 @@ bool Controller::configure(ros::NodeHandle& nh, const teb_local_planner::ObstCon
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teb_local_planner::RobotFootprintModelPtr robot_model, const std::vector<teb_local_planner::PoseSE2>& via_points)
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{
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//创建机器模型
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// 创建机器人动力学模型
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_dynamics = configureRobotDynamics(nh);
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if (!_dynamics) return false; // we may need state and control dimensions to check other parameters
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if (!_dynamics) return false; // 我们可能需要状态和控制维度来检查其他参数
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//离散网络，比如多重打靶法。参考点，输入，状态，等变量也会存放在grid里面，会实时更新。而且grid也继承了顶点传入到超图问题构建中
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_grid = configureGrid(nh);
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//求解器
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@ -74,23 +74,23 @@ bool Controller::configure(ros::NodeHandle& nh, const teb_local_planner::ObstCon
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nh.param("controller/outer_ocp_iterations", outer_ocp_iterations, outer_ocp_iterations);
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setNumOcpIterations(outer_ocp_iterations);
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// further goal opions
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// 进一步的目标选项
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nh.param("controller/force_reinit_new_goal_dist", _force_reinit_new_goal_dist, _force_reinit_new_goal_dist);
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nh.param("controller/force_reinit_new_goal_angular", _force_reinit_new_goal_angular, _force_reinit_new_goal_angular);
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nh.param("controller/allow_init_with_backward_motion", _guess_backwards_motion, _guess_backwards_motion);
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nh.param("controller/force_reinit_num_steps", _force_reinit_num_steps, _force_reinit_num_steps);
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// custom feedback:
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// 自定义反馈
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nh.param("controller/prefer_x_feedback", _prefer_x_feedback, _prefer_x_feedback);
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_x_feedback_sub = nh.subscribe("state_feedback", 1, &Controller::stateFeedbackCallback, this);
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// result publisher:
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// 结果发布
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_ocp_result_pub = nh.advertise<mpc_local_planner_msgs::OptimalControlResult>("ocp_result", 100);
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nh.param("controller/publish_ocp_results", _publish_ocp_results, _publish_ocp_results);
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nh.param("controller/print_cpu_time", _print_cpu_time, _print_cpu_time);
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setAutoUpdatePreviousControl(false); // we want to update the previous control value manually
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setAutoUpdatePreviousControl(false); // 我们希望手动更新之前的控制值
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if (_ocp->initialize())
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ROS_INFO("OCP initialized.");
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@ -111,14 +111,30 @@ bool Controller::step(const Controller::PoseSE2& start, const Controller::PoseSE
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return step(initial_plan, vel, dt, t, u_seq, x_seq);
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}
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/**
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* @brief 执行控制器的一步
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*
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* 该函数根据初始计划、当前速度、时间步长和当前时间执行控制器的一步操作，生成控制输入序列和状态序列。
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*
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* @param initial_plan 初始路径规划，由多个几何消息PoseStamped组成
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* @param vel 当前速度（Twist）
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* @param dt 时间步长（秒）
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* @param t 当前时间（ROS时间）
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* @param u_seq 输出控制输入序列（corbo::TimeSeries::Ptr）
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* @param x_seq 输出状态序列（corbo::TimeSeries::Ptr）
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* @return true 如果步骤成功执行
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* @return false 如果步骤执行失败
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*/
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bool Controller::step(const std::vector<geometry_msgs::PoseStamped>& initial_plan, const geometry_msgs::Twist& vel, double dt, ros::Time t,
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corbo::TimeSeries::Ptr u_seq, corbo::TimeSeries::Ptr x_seq)
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{
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// 检查控制器配置是否完整
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if (!_dynamics || !_grid || !_structured_ocp)
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{
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ROS_ERROR("Controller must be configured before invoking step().");
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return false;
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}
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// 检查初始计划至少包含两个姿态
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if (initial_plan.size() < 2)
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{
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ROS_ERROR("Controller::step(): initial plan must contain at least two poses.");
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@ -128,10 +144,11 @@ bool Controller::step(const std::vector<geometry_msgs::PoseStamped>& initial_pla
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PoseSE2 start(initial_plan.front().pose);
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PoseSE2 goal(initial_plan.back().pose);
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// 获取目标姿态的稳定状态
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Eigen::VectorXd xf(_dynamics->getStateDimension());
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_dynamics->getSteadyStateFromPoseSE2(goal, xf);
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// retrieve or estimate current state
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// 检索或估计当前状态
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Eigen::VectorXd x(_dynamics->getStateDimension());
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// check for new measurements
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bool new_x = false;
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@ -140,28 +157,28 @@ bool Controller::step(const std::vector<geometry_msgs::PoseStamped>& initial_pla
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new_x = _recent_x_feedback.size() > 0 && (t - _recent_x_time).toSec() < 2.0 * dt;
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if (new_x) x = _recent_x_feedback;
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}
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if (!new_x && (!_x_ts || _x_ts->isEmpty() || !_x_ts->getValuesInterpolate(dt, x))) // predict with previous state sequence
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if (!new_x && (!_x_ts || _x_ts->isEmpty() || !_x_ts->getValuesInterpolate(dt, x))) // 使用之前的状态序列预测
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{
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_dynamics->getSteadyStateFromPoseSE2(start, x); // otherwise initialize steady state
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_dynamics->getSteadyStateFromPoseSE2(start, x); // 否则初始化稳定状态
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}
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if (!new_x || !_prefer_x_feedback)
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{
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// Merge state feedback with odometry feedback if desired.
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// 如果需要，将状态反馈与里程反馈合并
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// Note, some models like unicycle overwrite the full state by odom feedback unless _prefer_x_measurement is set to true.
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_dynamics->mergeStateFeedbackAndOdomFeedback(start, vel, x);
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}
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// now check goal
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// 检查目标
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if (_force_reinit_num_steps > 0 && _ocp_seq % _force_reinit_num_steps == 0) _grid->clear();
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if (!_grid->isEmpty() && ((goal.position() - _last_goal.position()).norm() > _force_reinit_new_goal_dist ||
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std::abs(normalize_theta(goal.theta() - _last_goal.theta())) > _force_reinit_new_goal_angular))
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{
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// goal pose diverges a lot from the previous one, so force reinit
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// 如果目标姿态与之前的姿态差异较大，则强制重新初始化
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_grid->clear();
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}
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if (_grid->isEmpty())
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{
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// generate custom initialization based on initial_plan
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// 基于初始计划生成自定义初始化
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// check if the goal is behind the start pose (w.r.t. start orientation)
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bool backward = _guess_backwards_motion && (goal.position() - start.position()).dot(start.orientationUnitVec()) < 0;
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generateInitialStateTrajectory(x, xf, initial_plan, backward);
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@ -169,11 +186,12 @@ bool Controller::step(const std::vector<geometry_msgs::PoseStamped>& initial_pla
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corbo::Time time(t.toSec());
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_x_seq_init.setTimeFromStart(time);
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corbo::StaticReference xref(xf); // currently, we only support point-to-point transitions in ros
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corbo::StaticReference xref(xf); // 目前，我们仅支持在ros中进行点到点的转换
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corbo::ZeroReference uref(_dynamics->getInputDimension());
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// 执行预测控制器的一步
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_ocp_successful = PredictiveController::step(x, xref, uref, corbo::Duration(dt), time, u_seq, x_seq, nullptr, nullptr, &_x_seq_init);
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// publish results if desired
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// 如果需要，发布结果
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if (_publish_ocp_results) publishOptimalControlResult(); // TODO(roesmann): we could also pass time t from above
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ROS_INFO_STREAM_COND(_print_cpu_time, "Cpu time: " << _statistics.step_time.toSec() * 1000.0 << " ms.");
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++_ocp_seq;
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@ -225,34 +243,45 @@ void Controller::publishOptimalControlResult()
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void Controller::reset() { PredictiveController::reset(); }
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/**
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* @brief 配置离散网格
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*
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* 该函数根据参数服务器中的配置，选择并创建适当的离散网格。
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*
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* @param nh ROS节点句柄，用于获取参数
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* @return corbo::DiscretizationGridInterface::Ptr 返回创建的离散网格指针
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*/
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corbo::DiscretizationGridInterface::Ptr Controller::configureGrid(const ros::NodeHandle& nh)
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{
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// 如果动力学模型未配置，返回空指针
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if (!_dynamics) return {};
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// 默认网格类型为 "fd_grid"
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std::string grid_type = "fd_grid";
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nh.param("grid/type", grid_type, grid_type);
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if (grid_type == "fd_grid")
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{
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FiniteDifferencesGridSE2::Ptr grid;
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// 配置可变网格
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bool variable_grid = true;
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nh.param("grid/variable_grid/enable", variable_grid, variable_grid);
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if (variable_grid)
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{
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FiniteDifferencesVariableGridSE2::Ptr var_grid = std::make_shared<FiniteDifferencesVariableGridSE2>();
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// 从参数服务器中获取最小时间步长和最大时间步长
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double min_dt = 0.0;
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nh.param("grid/variable_grid/min_dt", min_dt, min_dt);
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double max_dt = 10.0;
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nh.param("grid/variable_grid/max_dt", max_dt, max_dt);
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var_grid->setDtBounds(min_dt, max_dt);
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// 从参数服务器中获取是否启用网格适应，默认值为 true
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bool grid_adaptation = true;
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nh.param("grid/variable_grid/grid_adaptation/enable", grid_adaptation, grid_adaptation);
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if (grid_adaptation)
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{
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// 从参数服务器中获取最大网格大小、时间步长滞后比率和最小网格大小
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int max_grid_size = 50;
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nh.param("grid/variable_grid/grid_adaptation/max_grid_size", max_grid_size, max_grid_size);
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double dt_hyst_ratio = 0.1;
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@ -273,15 +302,17 @@ corbo::DiscretizationGridInterface::Ptr Controller::configureGrid(const ros::Nod
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{
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grid = std::make_shared<FiniteDifferencesGridSE2>();
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}
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// common grid parameters
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// 公共网格参数
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// 从参数服务器中获取网格参考大小，默认值为 20
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int grid_size_ref = 20;
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nh.param("grid/grid_size_ref", grid_size_ref, grid_size_ref);
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grid->setNRef(grid_size_ref);
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// 从参数服务器中获取时间步长参考值，默认值为 0.3
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double dt_ref = 0.3;
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nh.param("grid/dt_ref", dt_ref, dt_ref);
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grid->setDtRef(dt_ref);
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// 从参数服务器中获取状态固定向量，默认值为 {true, true, true}
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std::vector<bool> xf_fixed = {true, true, true};
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nh.param("grid/xf_fixed", xf_fixed, xf_fixed);
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if ((int)xf_fixed.size() != _dynamics->getStateDimension())
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@ -293,11 +324,11 @@ corbo::DiscretizationGridInterface::Ptr Controller::configureGrid(const ros::Nod
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Eigen::Matrix<bool, -1, 1> xf_fixed_eigen(xf_fixed.size()); // we cannot use Eigen::Map as vector<bool> does not provide raw access
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for (int i = 0; i < (int)xf_fixed.size(); ++i) xf_fixed_eigen[i] = xf_fixed[i];
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grid->setXfFixed(xf_fixed_eigen);
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// 从参数服务器中获取是否启用热启动，默认值为 true
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bool warm_start = true;
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nh.param("grid/warm_start", warm_start, warm_start);
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grid->setWarmStart(warm_start);
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// 从参数服务器中获取配点方法，默认值为 "forward_differences"
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std::string collocation_method = "forward_differences";
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nh.param("grid/collocation_method", collocation_method, collocation_method);
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@ -318,6 +349,7 @@ corbo::DiscretizationGridInterface::Ptr Controller::configureGrid(const ros::Nod
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ROS_ERROR_STREAM("Unknown collocation method '" << collocation_method << "' specified. Falling back to default...");
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}
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// 从参数服务器中获取代价积分方法，默认值为 "left_sum"
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std::string cost_integration_method = "left_sum";
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nh.param("grid/cost_integration_method", cost_integration_method, cost_integration_method);
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@ -344,21 +376,33 @@ corbo::DiscretizationGridInterface::Ptr Controller::configureGrid(const ros::Nod
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return {};
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}
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/**
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* @brief 配置机器人动力学模型
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*
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* 该函数根据参数服务器中的配置，选择并创建适当的机器人动力学模型。
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*
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* @param nh ROS节点句柄，用于获取参数
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* @return RobotDynamicsInterface::Ptr 返回创建的机器人动力学模型指针
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*/
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RobotDynamicsInterface::Ptr Controller::configureRobotDynamics(const ros::NodeHandle& nh)
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{
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// 默认机器人类型为 "unicycle"（单轮车模型）
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_robot_type = "unicycle";
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nh.param("robot/type", _robot_type, _robot_type);
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if (_robot_type == "unicycle")
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{
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// 如果机器人类型为 "unicycle"，返回 UnicycleModel 实例
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return std::make_shared<UnicycleModel>();
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}
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else if (_robot_type == "simple_car")
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{
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// 如果机器人类型为 "simple_car"（简单汽车模型）
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double wheelbase = 0.5;
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nh.param("robot/simple_car/wheelbase", wheelbase, wheelbase);
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bool front_wheel_driving = false;
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nh.param("robot/simple_car/front_wheel_driving", front_wheel_driving, front_wheel_driving);
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// 如果前轮驱动，返回 SimpleCarFrontWheelDrivingModel 实例
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if (front_wheel_driving)
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return std::make_shared<SimpleCarFrontWheelDrivingModel>(wheelbase);
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else
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@ -366,6 +410,7 @@ RobotDynamicsInterface::Ptr Controller::configureRobotDynamics(const ros::NodeHa
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}
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else if (_robot_type == "kinematic_bicycle_vel_input")
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{
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// 如果机器人类型为 "kinematic_bicycle_vel_input"（运动学自行车模型，速度输入）
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double length_rear = 1.0;
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nh.param("robot/kinematic_bicycle_vel_input/length_rear", length_rear, length_rear);
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double length_front = 1.0;
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@ -374,32 +419,44 @@ RobotDynamicsInterface::Ptr Controller::configureRobotDynamics(const ros::NodeHa
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}
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else
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{
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// 未知的机器人类型，输出错误信息
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ROS_ERROR_STREAM("Unknown robot type '" << _robot_type << "' specified.");
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}
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return {};
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}
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/**
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* @brief 配置非线性规划（NLP）求解器
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*
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* 该函数根据参数服务器中的配置，选择并创建适当的NLP求解器。
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*
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* @param nh ROS节点句柄，用于获取参数
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* @return corbo::NlpSolverInterface::Ptr 返回创建的NLP求解器指针
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*/
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corbo::NlpSolverInterface::Ptr Controller::configureSolver(const ros::NodeHandle& nh)
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{
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// 从参数服务器中获取求解器类型，默认值为 "ipopt"
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std::string solver_type = "ipopt";
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nh.param("solver/type", solver_type, solver_type);
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if (solver_type == "ipopt")
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{
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// 配置Ipopt求解器
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corbo::SolverIpopt::Ptr solver = std::make_shared<corbo::SolverIpopt>();
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solver->initialize(); // requried for setting parameters afterward
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solver->initialize(); // 初始化以便之后设置参数
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// 从参数服务器中获取Ipopt求解器的迭代次数，默认值为100
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int iterations = 100;
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nh.param("solver/ipopt/iterations", iterations, iterations);
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solver->setIterations(iterations);
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// 从参数服务器中获取Ipopt求解器的最大CPU时间，默认值为-1.0
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double max_cpu_time = -1.0;
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nh.param("solver/ipopt/max_cpu_time", max_cpu_time, max_cpu_time);
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solver->setMaxCpuTime(max_cpu_time);
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// now check for additional ipopt options
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// 设置其他Ipopt选项
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std::map<std::string, double> numeric_options;
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nh.param("solver/ipopt/ipopt_numeric_options", numeric_options, numeric_options);
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for (const auto& item : numeric_options)
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@ -441,12 +498,15 @@ corbo::NlpSolverInterface::Ptr Controller::configureSolver(const ros::NodeHandle
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// }
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else if (solver_type == "lsq_lm")
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{
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// 配置Levenberg-Marquardt求解器
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corbo::LevenbergMarquardtSparse::Ptr solver = std::make_shared<corbo::LevenbergMarquardtSparse>();
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// 从参数服务器中获取Levenberg-Marquardt求解器的迭代次数，默认值为10
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int iterations = 10;
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nh.param("solver/lsq_lm/iterations", iterations, iterations);
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solver->setIterations(iterations);
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// 从参数服务器中获取初始惩罚权重
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double weight_init_eq = 2;
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nh.param("solver/lsq_lm/weight_init_eq", weight_init_eq, weight_init_eq);
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double weight_init_ineq = 2;
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@ -456,6 +516,7 @@ corbo::NlpSolverInterface::Ptr Controller::configureSolver(const ros::NodeHandle
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solver->setPenaltyWeights(weight_init_eq, weight_init_ineq, weight_init_bounds);
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// 从参数服务器中获取权重适应因子
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double weight_adapt_factor_eq = 1;
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nh.param("solver/lsq_lm/weight_adapt_factor_eq", weight_adapt_factor_eq, weight_adapt_factor_eq);
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double weight_adapt_factor_ineq = 1;
|
||||
|
|
@ -463,6 +524,7 @@ corbo::NlpSolverInterface::Ptr Controller::configureSolver(const ros::NodeHandle
|
|||
double weight_adapt_factor_bounds = 1;
|
||||
nh.param("solver/lsq_lm/weight_adapt_factor_bounds", weight_adapt_factor_bounds, weight_adapt_factor_bounds);
|
||||
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||||
// 从参数服务器中获取最大权重适应值
|
||||
double weight_adapt_max_eq = 500;
|
||||
nh.param("solver/lsq_lm/weight_adapt_max_eq", weight_adapt_max_eq, weight_adapt_max_eq);
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double weight_adapt_max_ineq = 500;
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||||
|
|
@ -483,17 +545,30 @@ corbo::NlpSolverInterface::Ptr Controller::configureSolver(const ros::NodeHandle
|
|||
return {};
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief 配置结构化最优控制问题（OCP）
|
||||
*
|
||||
* 该函数根据参数服务器中的配置，选择并创建适当的OCP。
|
||||
*
|
||||
* @param nh ROS节点句柄，用于获取参数
|
||||
* @param obstacles 障碍物容器
|
||||
* @param robot_model 机器人轮廓模型
|
||||
* @param via_points 中间目标点
|
||||
* @return corbo::StructuredOptimalControlProblem::Ptr 返回创建的OCP指针
|
||||
*/
|
||||
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)
|
||||
{
|
||||
// 创建超图优化问题
|
||||
corbo::BaseHyperGraphOptimizationProblem::Ptr hg = std::make_shared<corbo::HyperGraphOptimizationProblemEdgeBased>();
|
||||
|
||||
// 创建结构化最优控制问题实例
|
||||
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();
|
||||
const int x_dim = _dynamics->getStateDimension(); // 状态维度
|
||||
const int u_dim = _dynamics->getInputDimension(); // 控制输入维度
|
||||
|
||||
// 根据机器人类型配置控制输入的上下界
|
||||
if (_robot_type == "unicycle")
|
||||
{
|
||||
double max_vel_x = 0.4;
|
||||
|
|
@ -551,6 +626,7 @@ corbo::StructuredOptimalControlProblem::Ptr Controller::configureOcp(const ros::
|
|||
return {};
|
||||
}
|
||||
|
||||
// 配置阶段成本（stage cost）
|
||||
std::string objective_type = "minimum_time";
|
||||
nh.param("planning/objective/type", objective_type, objective_type);
|
||||
bool lsq_solver = _solver->isLsqSolver();
|
||||
|
|
@ -643,6 +719,7 @@ corbo::StructuredOptimalControlProblem::Ptr Controller::configureOcp(const ros::
|
|||
return {};
|
||||
}
|
||||
|
||||
// 配置终端成本（terminal cost）
|
||||
std::string terminal_cost = "none";
|
||||
nh.param("planning/terminal_cost/type", terminal_cost, terminal_cost);
|
||||
|
||||
|
|
@ -676,6 +753,7 @@ corbo::StructuredOptimalControlProblem::Ptr Controller::configureOcp(const ros::
|
|||
return {};
|
||||
}
|
||||
|
||||
// 配置终端约束（terminal constraint）
|
||||
std::string terminal_constraint = "none";
|
||||
nh.param("planning/terminal_constraint/type", terminal_constraint, terminal_constraint);
|
||||
|
||||
|
|
@ -711,24 +789,24 @@ corbo::StructuredOptimalControlProblem::Ptr Controller::configureOcp(const ros::
|
|||
return {};
|
||||
}
|
||||
|
||||
// 配置阶段不等式约束（stage inequality constraint）
|
||||
_inequality_constraint = std::make_shared<StageInequalitySE2>();
|
||||
_inequality_constraint->setObstacleVector(obstacles);
|
||||
_inequality_constraint->setRobotFootprintModel(robot_model);
|
||||
|
||||
// configure collision avoidance
|
||||
|
||||
// 配置碰撞避免参数
|
||||
double min_obstacle_dist = 0.5;
|
||||
nh.param("collision_avoidance/min_obstacle_dist", min_obstacle_dist, min_obstacle_dist);
|
||||
nh.param("collision/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);
|
||||
nh.param("collision/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);
|
||||
nh.param("collision/force_inclusion_dist", force_inclusion_dist, force_inclusion_dist);
|
||||
double cutoff_dist = 2;
|
||||
nh.param("collision_avoidance/cutoff_dist", cutoff_dist, cutoff_dist);
|
||||
nh.param("collision/cutoff_dist", cutoff_dist, cutoff_dist);
|
||||
_inequality_constraint->setObstacleFilterParameters(force_inclusion_dist, cutoff_dist);
|
||||
|
||||
// configure control deviation bounds
|
||||
|
|
@ -879,7 +957,6 @@ bool Controller::isPoseTrajectoryFeasible(base_local_planner::CostmapModel* cost
|
|||
ROS_ERROR("isPoseTrajectoriyFeasible is currently only implemented for fd grids");
|
||||
return true;
|
||||
}
|
||||
ROS_INFO(" look_ahead_idx:%d",look_ahead_idx);
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -351,7 +351,7 @@ uint32_t MpcLocalPlannerROS::computeVelocityCommands(const geometry_msgs::PoseSt
|
|||
_robot_vel.linear.y = robot_vel_tf.pose.position.y;
|
||||
_robot_vel.angular.z = tf2::getYaw(robot_vel_tf.pose.orientation);
|
||||
|
||||
// 修剪全局计划，剪掉机器人之前的部分
|
||||
// 修剪全局计划，剪掉机器人之前的部分，从global的头部开始查找，直到距离机器人小于global_plan_prune_distance
|
||||
pruneGlobalPlan(*_tf, robot_pose, _global_plan, _params.global_plan_prune_distance);
|
||||
|
||||
// Convert To Local frame
|
||||
|
|
@ -366,7 +366,7 @@ uint32_t MpcLocalPlannerROS::computeVelocityCommands(const geometry_msgs::PoseSt
|
|||
return mbf_msgs::ExePathResult::INTERNAL_ERROR;
|
||||
}
|
||||
|
||||
// 更新路径点容器
|
||||
// 更新路径点容器，根据global_plan_viapoint_sep间隔距离，向_viapoint中插入路径点
|
||||
if (!_custom_via_points_active) updateViaPointsContainer(transformed_plan, _params.global_plan_viapoint_sep);
|
||||
|
||||
// 检查是否到达全局目标
|
||||
|
|
@ -561,6 +561,7 @@ void MpcLocalPlannerROS::updateObstacleContainerWithCostmap()
|
|||
|
||||
// 检查障碍物是否在机器人前方（例如，不远离机器人）
|
||||
Eigen::Vector2d obs_dir = obs - _robot_pose.position();
|
||||
// 如果障礙物在機器人後方，而且距離大於參數設置的距離，則忽略
|
||||
if (obs_dir.dot(robot_orient) < 0 && obs_dir.norm() > _params.costmap_obstacles_behind_robot_dist) continue;
|
||||
|
||||
_obstacles.push_back(ObstaclePtr(new PointObstacle(obs)));
|
||||
|
|
@ -704,7 +705,7 @@ void MpcLocalPlannerROS::updateObstacleContainerWithCustomObstacles()
|
|||
* 该函数根据转换后的计划和最小间隔距离更新路径点容器。
|
||||
*
|
||||
* @param transformed_plan 转换后的全局计划。
|
||||
* @param min_separation 路径点之间的最小间隔距离。
|
||||
* @param min_separation 路径点之间的最小间隔距离。默认为-1,表示不使用路径点。
|
||||
*/
|
||||
void MpcLocalPlannerROS::updateViaPointsContainer(const std::vector<geometry_msgs::PoseStamped>& transformed_plan, double min_separation)
|
||||
{
|
||||
|
|
@ -847,7 +848,7 @@ bool MpcLocalPlannerROS::transformGlobalPlan(const tf2_ros::Buffer& tf, const st
|
|||
double sq_dist_threshold = dist_threshold * dist_threshold;
|
||||
double sq_dist = 1e10;
|
||||
|
||||
// 我们需要循环直到找到一个距离机器人一定距离内的计划点
|
||||
// 我们需要循环直到找到一个距离机器人最近的路径点
|
||||
for (int j = 0; j < (int)global_plan.size(); ++j)
|
||||
{
|
||||
double x_diff = robot_pose.pose.position.x - global_plan[j].pose.position.x;
|
||||
|
|
@ -866,7 +867,7 @@ bool MpcLocalPlannerROS::transformGlobalPlan(const tf2_ros::Buffer& tf, const st
|
|||
|
||||
double plan_length = 0; // 检查沿计划的累计欧几里得距离
|
||||
|
||||
// 现在我们将转换，直到点超出我们的距离阈值
|
||||
// 现在我们将转换，直到点超出我们的距离阈值，从距离机器人的最近的点开始，直到距离满足max_plan_length
|
||||
while (i < (int)global_plan.size() && sq_dist <= sq_dist_threshold && (max_plan_length <= 0 || plan_length <= max_plan_length))
|
||||
{
|
||||
const geometry_msgs::PoseStamped& pose = global_plan[i];
|
||||
|
|
|
|||
Loading…
Reference in New Issue