function [reftraj_x,reftraj_y,reftraj_theta,reftraj_delta]=funccircularrefer
时间: 2024-02-04 13:01:06 浏览: 19
函数funccircularrefer用于生成圆形轨迹的参考值。圆形轨迹的参数包括圆心位置、半径和起始角度。
首先,我们需要定义圆心的位置和半径。然后根据圆形轨迹的参数,计算出轨迹上各点的x坐标、y坐标和航向角。
接着,根据计算出的x坐标、y坐标和航向角,我们可以得到轨迹的参考值。最后,将计算得到的参考值分别存储在reftraj_x、reftraj_y、reftraj_theta和reftraj_delta中,并返回给调用者。
函数funccircularrefer的输入参数包括圆心位置、半径和起始角度,输出参数包括参考轨迹的x坐标、y坐标、航向角和舵角值。
通过这个函数,我们可以方便地获取圆形轨迹的参考值,为后续的控制和路径规划提供便利。
相关问题
给下列程序添加注释: void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) { if (setup_ && config.restore_defaults) { config = default_config_; config.restore_defaults = false; } if ( ! setup_) { default_config_ = config; setup_ = true; } // update generic local planner params base_local_planner::LocalPlannerLimits limits; limits.max_vel_trans = config.max_vel_trans; limits.min_vel_trans = config.min_vel_trans; limits.max_vel_x = config.max_vel_x; limits.min_vel_x = config.min_vel_x; limits.max_vel_y = config.max_vel_y; limits.min_vel_y = config.min_vel_y; limits.max_vel_theta = config.max_vel_theta; limits.min_vel_theta = config.min_vel_theta; limits.acc_lim_x = config.acc_lim_x; limits.acc_lim_y = config.acc_lim_y; limits.acc_lim_theta = config.acc_lim_theta; limits.acc_lim_trans = config.acc_lim_trans; limits.xy_goal_tolerance = config.xy_goal_tolerance; limits.yaw_goal_tolerance = config.yaw_goal_tolerance; limits.prune_plan = config.prune_plan; limits.trans_stopped_vel = config.trans_stopped_vel; limits.theta_stopped_vel = config.theta_stopped_vel; planner_util_.reconfigureCB(limits, config.restore_defaults); // update dwa specific configuration dp_->reconfigure(config); }
/**
* @brief Callback function for dynamic reconfiguration of DWA planner parameters
*
* @param config Reference to the configuration object that stores the updated parameters
* @param level The level of reconfiguration, unused in this function
*/
void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) {
// If the setup has been completed and restore_defaults flag is set, restore default configuration
if (setup_ && config.restore_defaults) {
config = default_config_;
config.restore_defaults = false;
}
// If setup has not been completed, store default configuration and set the setup flag to true
if ( ! setup_) {
default_config_ = config;
setup_ = true;
}
// Update generic local planner parameters
base_local_planner::LocalPlannerLimits limits;
limits.max_vel_trans = config.max_vel_trans;
limits.min_vel_trans = config.min_vel_trans;
limits.max_vel_x = config.max_vel_x;
limits.min_vel_x = config.min_vel_x;
limits.max_vel_y = config.max_vel_y;
limits.min_vel_y = config.min_vel_y;
limits.max_vel_theta = config.max_vel_theta;
limits.min_vel_theta = config.min_vel_theta;
limits.acc_lim_x = config.acc_lim_x;
limits.acc_lim_y = config.acc_lim_y;
limits.acc_lim_theta = config.acc_lim_theta;
limits.acc_lim_trans = config.acc_lim_trans;
limits.xy_goal_tolerance = config.xy_goal_tolerance;
limits.yaw_goal_tolerance = config.yaw_goal_tolerance;
limits.prune_plan = config.prune_plan;
limits.trans_stopped_vel = config.trans_stopped_vel;
limits.theta_stopped_vel = config.theta_stopped_vel;
// Call reconfigureCB function of the planner_util_ object with updated limits and restore_defaults flag
planner_util_.reconfigureCB(limits, config.restore_defaults);
// Call reconfigure function of the dp_ object with updated configuration
dp_->reconfigure(config);
}
cost, grad= costFunction(initial_theta,X,y)
这段代码看起来是在调用 `costFunction` 函数,传入了初始参数 `initial_theta`、特征矩阵 `X` 和标签向量 `y`,并且将函数返回的代价值和梯度值分别赋值给 `cost` 和 `grad`。
可能的实现如下:
```python
def costFunction(theta, X, y):
# 计算代价函数和梯度
m = len(y)
h = sigmoid(X @ theta)
J = -1/m * (y.T @ np.log(h) + (1 - y).T @ np.log(1 - h))
grad = 1/m * X.T @ (h - y)
return J, grad
# 调用 costFunction
initial_theta = np.zeros((n, 1))
cost, grad = costFunction(initial_theta, X, y)
```
其中,`sigmoid` 函数是一个常用的逻辑斯蒂函数,实现如下:
```python
def sigmoid(z):
return 1 / (1 + np.exp(-z))
```
需要注意的是,这里的 `@` 符号表示矩阵乘法,`np.log` 表示自然对数。