【ROS】The Learnning of ROS Navigation Source Code/09-17
崔恺
1.amcl
2.base_local_planner
3.carrot_planner
3.1头文件
#include <ros/ros.h>
#include <costmap_2d/costmap_2d_ros.h>
#include <costmap_2d/costmap_2d.h>
#include <nav_core/base_global_planner.h>
#include <geometry_msgs/PoseStamped.h>
#include <angles/angles.h>
#include <base_local_planner/world_model.h>
#include <base_local_planner/costmap_model.h>
ros/ros.h
costmap_2d/costmap_2d_ros.h:
costmap_2d::Costmap2DROS maintains much of the ROS related functionality. It contains a costmap_2d::LayeredCostmap which is used to keep track of each of the layers. Each layer is instantiated in the Costmap2DROS using pluginlib and is added to the LayeredCostmap. The layers themselves may be compiled individually, allowing arbitrary changes to the costmap to be made through the C++ interface.
costmap_2d/costmap_2d.h
The costmap_2d/costmap_2d.hcostmap_2d::Costmap2D class implements the basic data structure for storing and accessing the two dimensional costmap.
geometry_msgs/PoseStamped.h
A Pose with reference coordinate frame and timestamp
base_local_planner/world_model.h
Check a footprint at a given position and orientation for legality in the world
base_local_planner/costmap_model.h
A class that implements the WorldModel interface to provide grid based collision checks for the trajectory controller using the costmap.
3.2test_planner.h
namespace test_planner{
//命名空间 为test_planner
/**
* @class testPlanner
* @brief Provides a simple global planner that will compute a valid goal point for the local planner by walking back along the vector between the robot and the user-specified goal point until a valid cost is found.
*/
class testPlanner : public nav_core::BaseGlobalPlanner {//定义一个全局规划器testPlanner,基类为nav_core::BaseGlobalPlanner
public:
/**
* @brief Constructor for the testPlanner
*/
testPlanner();
/**
* @brief Constructor for the testPlanner
* @param name The name of this planner
* @param costmap_ros A pointer to the ROS wrapper of the costmap to use for planning
*/
testPlanner(std::string name, costmap_2d::Costmap2DROS* costmap_ros);
/**
* @brief Initialization function for the testPlanner
* @param name The name of this planner
* @param costmap_ros A pointer to the ROS wrapper of the costmap to use for planning
*/
void initialize(std::string name, costmap_2d::Costmap2DROS* costmap_ros);
/**
* @brief Given a goal pose in the world, compute a plan
* @param start The start pose
* @param goal The goal pose
* @param plan The plan... filled by the planner
* @return True if a valid plan was found, false otherwise
*/
bool makePlan(const geometry_msgs::PoseStamped& start,
const geometry_msgs::PoseStamped& goal, std::vector<geometry_msgs::PoseStamped>& plan);
private:
costmap_2d::Costmap2DROS* costmap_ros_;
double step_size_, min_dist_from_robot_;
costmap_2d::Costmap2D* costmap_;
base_local_planner::WorldModel* world_model_; ///< @brief The world model that the controller will use
void publishGlobalPlan(std::vector<geometry_msgs::PoseStamped>& path);
// for visualisation, publishers of global and local plan
ros::Publisher g_plan_pub_;
/**
* @brief Checks the legality of the robot footprint at a position and orientation using the world model
* @param x_i The x position of the robot
* @param y_i The y position of the robot
* @param theta_i The orientation of the robot
* @return
*/
double footprintCost(double x_i, double y_i, double theta_i);
bool initialized_;
};
};
4.clear_costmap_recovery
5.costmap_2d
6.dwa_local_planner
6.1 dwa_planner.cpp
头文件
#include <dwa_local_planner/dwa_planner.h> //dwa.h
#include <base_local_planner/goal_functions.h>//与目标有关
#include <cmath>//数学函数
//for computing path distance
#include <queue>//队列
#include <angles/angles.h>
#include <ros/ros.h>
#include <tf2/utils.h>//坐标变换
#include <sensor_msgs/PointCloud2.h>//点云
#include <sensor_msgs/point_cloud2_iterator.h>
代码
/*********************************************************************
*
* Software License Agreement (BSD License)
*
* Copyright (c) 2009, Willow Garage, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of Willow Garage, Inc. nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* Author: Eitan Marder-Eppstein
*********************************************************************/
#include <dwa_local_planner/dwa_planner.h>
#include <base_local_planner/goal_functions.h>
#include <cmath>
//for computing path distance
#include <queue>
#include <angles/angles.h>
#include <ros/ros.h>
#include <tf2/utils.h>
#include <sensor_msgs/PointCloud2.h>
#include <sensor_msgs/point_cloud2_iterator.h>
namespace dwa_local_planner {
void DWAPlanner::reconfigure(DWAPlannerConfig &config)
{
boost::mutex::scoped_lock l(configuration_mutex_);//锁存
generator_.setParameters(
config.sim_time,
config.sim_granularity,
config.angular_sim_granularity,
config.use_dwa,
sim_period_);
double resolution = planner_util_->getCostmap()->getResolution();
path_distance_bias_ = resolution * config.path_distance_bias;
// pdistscale used for both path and alignment, set forward_point_distance to zero to discard alignment
path_costs_.setScale(path_distance_bias_);
alignment_costs_.setScale(path_distance_bias_);
goal_distance_bias_ = resolution * config.goal_distance_bias;
goal_costs_.setScale(goal_distance_bias_);
goal_front_costs_.setScale(goal_distance_bias_);
occdist_scale_ = config.occdist_scale;
obstacle_costs_.setScale(occdist_scale_);
stop_time_buffer_ = config.stop_time_buffer;
oscillation_costs_.setOscillationResetDist(config.oscillation_reset_dist, config.oscillation_reset_angle);
forward_point_distance_ = config.forward_point_distance;
goal_front_costs_.setXShift(forward_point_distance_);
alignment_costs_.setXShift(forward_point_distance_);
// obstacle costs can vary due to scaling footprint feature
obstacle_costs_.setParams(config.max_vel_trans, config.max_scaling_factor, config.scaling_speed);
twirling_costs_.setScale(config.twirling_scale);
int vx_samp, vy_samp, vth_samp;
vx_samp = config.vx_samples;
vy_samp = config.vy_samples;
vth_samp = config.vth_samples;
if (vx_samp <= 0) {
ROS_WARN("You've specified that you don't want any samples in the x dimension. We'll at least assume that you want to sample one value... so we're going to set vx_samples to 1 instead");
vx_samp = 1;
config.vx_samples = vx_samp;
}
if (vy_samp <= 0) {
ROS_WARN("You've specified that you don't want any samples in the y dimension. We'll at least assume that you want to sample one value... so we're going to set vy_samples to 1 instead");
vy_samp = 1;
config.vy_samples = vy_samp;
}
if (vth_samp <= 0) {
ROS_WARN("You've specified that you don't want any samples in the th dimension. We'll at least assume that you want to sample one value... so we're going to set vth_samples to 1 instead");
vth_samp = 1;
config.vth_samples = vth_samp;
}
vsamples_[0] = vx_samp;
vsamples_[1] = vy_samp;
vsamples_[2] = vth_samp;
}
DWAPlanner::DWAPlanner(std::string name, base_local_planner::LocalPlannerUtil *planner_util) :
planner_util_(planner_util),
obstacle_costs_(planner_util->getCostmap()),
path_costs_(planner_util->getCostmap()),
goal_costs_(planner_util->getCostmap(), 0.0, 0.0, true),
goal_front_costs_(planner_util->getCostmap(), 0.0, 0.0, true),
alignment_costs_(planner_util->getCostmap())
{
ros::NodeHandle private_nh("~/" + name);
goal_front_costs_.setStopOnFailure( false );
alignment_costs_.setStopOnFailure( false );
//Assuming this planner is being run within the navigation stack, we can
//just do an upward search for the frequency at which its being run. This
//also allows the frequency to be overwritten locally.
std::string controller_frequency_param_name;
if(!private_nh.searchParam("controller_frequency", controller_frequency_param_name)) {
sim_period_ = 0.05;
} else {
double controller_frequency = 0;
private_nh.param(controller_frequency_param_name, controller_frequency, 20.0);
if(controller_frequency > 0) {
sim_period_ = 1.0 / controller_frequency;
} else {
ROS_WARN("A controller_frequency less than 0 has been set. Ignoring the parameter, assuming a rate of 20Hz");
sim_period_ = 0.05;
}
}
ROS_INFO("Sim period is set to %.2f", sim_period_);
oscillation_costs_.resetOscillationFlags();
bool sum_scores;
private_nh.param("sum_scores", sum_scores, false);
obstacle_costs_.setSumScores(sum_scores);
private_nh.param("publish_cost_grid_pc", publish_cost_grid_pc_, false);
map_viz_.initialize(name, planner_util->getGlobalFrame(), boost::bind(&DWAPlanner::getCellCosts, this, _1, _2, _3, _4, _5, _6));
private_nh.param("global_frame_id", frame_id_, std::string("odom"));
traj_cloud_pub_ = private_nh.advertise<sensor_msgs::PointCloud2>("trajectory_cloud", 1);
private_nh.param("publish_traj_pc", publish_traj_pc_, false);
// set up all the cost functions that will be applied in order
// (any function returning negative values will abort scoring, so the order can improve performance)
std::vector<base_local_planner::TrajectoryCostFunction*> critics;
critics.push_back(&oscillation_costs_); // discards oscillating motions (assisgns cost -1)
critics.push_back(&obstacle_costs_); // discards trajectories that move into obstacles
critics.push_back(&goal_front_costs_); // prefers trajectories that make the nose go towards (local) nose goal
critics.push_back(&alignment_costs_); // prefers trajectories that keep the robot nose on nose path
critics.push_back(&path_costs_); // prefers trajectories on global path
critics.push_back(&goal_costs_); // prefers trajectories that go towards (local) goal, based on wave propagation
critics.push_back(&twirling_costs_); // optionally prefer trajectories that don't spin
// trajectory generators
std::vector<base_local_planner::TrajectorySampleGenerator*> generator_list;
generator_list.push_back(&generator_);
scored_sampling_planner_ = base_local_planner::SimpleScoredSamplingPlanner(generator_list, critics);
private_nh.param("cheat_factor", cheat_factor_, 1.0);
}
// used for visualization only, total_costs are not really total costs
bool DWAPlanner::getCellCosts(int cx, int cy, float &path_cost, float &goal_cost, float &occ_cost, float &total_cost) {
path_cost = path_costs_.getCellCosts(cx, cy);
goal_cost = goal_costs_.getCellCosts(cx, cy);
occ_cost = planner_util_->getCostmap()->getCost(cx, cy);
if (path_cost == path_costs_.obstacleCosts() ||
path_cost == path_costs_.unreachableCellCosts() ||
occ_cost >= costmap_2d::INSCRIBED_INFLATED_OBSTACLE) {
return false;
}
total_cost =
path_distance_bias_ * path_cost +
goal_distance_bias_ * goal_cost +
occdist_scale_ * occ_cost;
return true;
}
bool DWAPlanner::setPlan(const std::vector<geometry_msgs::PoseStamped>& orig_global_plan) {
oscillation_costs_.resetOscillationFlags();
return planner_util_->setPlan(orig_global_plan);
}
/**
* This function is used when other strategies are to be applied,
* but the cost functions for obstacles are to be reused.
*/
bool DWAPlanner::checkTrajectory(
Eigen::Vector3f pos,
Eigen::Vector3f vel,
Eigen::Vector3f vel_samples){
oscillation_costs_.resetOscillationFlags();
base_local_planner::Trajectory traj;
geometry_msgs::PoseStamped goal_pose = global_plan_.back();
Eigen::Vector3f goal(goal_pose.pose.position.x, goal_pose.pose.position.y, tf2::getYaw(goal_pose.pose.orientation));
base_local_planner::LocalPlannerLimits limits = planner_util_->getCurrentLimits();
generator_.initialise(pos,
vel,
goal,
&limits,
vsamples_);
generator_.generateTrajectory(pos, vel, vel_samples, traj);
double cost = scored_sampling_planner_.scoreTrajectory(traj, -1);
//if the trajectory is a legal one... the check passes
if(cost >= 0) {
return true;
}
ROS_WARN("Invalid Trajectory %f, %f, %f, cost: %f", vel_samples[0], vel_samples[1], vel_samples[2], cost);
//otherwise the check fails
return false;
}
void DWAPlanner::updatePlanAndLocalCosts(
const geometry_msgs::PoseStamped& global_pose,
const std::vector<geometry_msgs::PoseStamped>& new_plan,
const std::vector<geometry_msgs::Point>& footprint_spec) {
global_plan_.resize(new_plan.size());
for (unsigned int i = 0; i < new_plan.size(); ++i) {
global_plan_[i] = new_plan[i];
}
obstacle_costs_.setFootprint(footprint_spec);
// costs for going away from path
path_costs_.setTargetPoses(global_plan_);
// costs for not going towards the local goal as much as possible
goal_costs_.setTargetPoses(global_plan_);
// alignment costs
geometry_msgs::PoseStamped goal_pose = global_plan_.back();
Eigen::Vector3f pos(global_pose.pose.position.x, global_pose.pose.position.y, tf2::getYaw(global_pose.pose.orientation));
double sq_dist =
(pos[0] - goal_pose.pose.position.x) * (pos[0] - goal_pose.pose.position.x) +
(pos[1] - goal_pose.pose.position.y) * (pos[1] - goal_pose.pose.position.y);
// we want the robot nose to be drawn to its final position
// (before robot turns towards goal orientation), not the end of the
// path for the robot center. Choosing the final position after
// turning towards goal orientation causes instability when the
// robot needs to make a 180 degree turn at the end
std::vector<geometry_msgs::PoseStamped> front_global_plan = global_plan_;
double angle_to_goal = atan2(goal_pose.pose.position.y - pos[1], goal_pose.pose.position.x - pos[0]);
front_global_plan.back().pose.position.x = front_global_plan.back().pose.position.x +
forward_point_distance_ * cos(angle_to_goal);
front_global_plan.back().pose.position.y = front_global_plan.back().pose.position.y + forward_point_distance_ *
sin(angle_to_goal);
goal_front_costs_.setTargetPoses(front_global_plan);
// keeping the nose on the path
if (sq_dist > forward_point_distance_ * forward_point_distance_ * cheat_factor_) {
alignment_costs_.setScale(path_distance_bias_);
// costs for robot being aligned with path (nose on path, not ju
alignment_costs_.setTargetPoses(global_plan_);
} else {
// once we are close to goal, trying to keep the nose close to anything destabilizes behavior.
alignment_costs_.setScale(0.0);
}
}
/*
* given the current state of the robot, find a good trajectory
*/
base_local_planner::Trajectory DWAPlanner::findBestPath(
const geometry_msgs::PoseStamped& global_pose,
const geometry_msgs::PoseStamped& global_vel,
geometry_msgs::PoseStamped& drive_velocities) {
//make sure that our configuration doesn't change mid-run
boost::mutex::scoped_lock l(configuration_mutex_);
Eigen::Vector3f pos(global_pose.pose.position.x, global_pose.pose.position.y, tf2::getYaw(global_pose.pose.orientation));
Eigen::Vector3f vel(global_vel.pose.position.x, global_vel.pose.position.y, tf2::getYaw(global_vel.pose.orientation));
geometry_msgs::PoseStamped goal_pose = global_plan_.back();
Eigen::Vector3f goal(goal_pose.pose.position.x, goal_pose.pose.position.y, tf2::getYaw(goal_pose.pose.orientation));
base_local_planner::LocalPlannerLimits limits = planner_util_->getCurrentLimits();
// prepare cost functions and generators for this run
generator_.initialise(pos,
vel,
goal,
&limits,
vsamples_);
result_traj_.cost_ = -7;
// find best trajectory by sampling and scoring the samples
std::vector<base_local_planner::Trajectory> all_explored;
scored_sampling_planner_.findBestTrajectory(result_traj_, &all_explored);
if(publish_traj_pc_)
{
sensor_msgs::PointCloud2 traj_cloud;
traj_cloud.header.frame_id = frame_id_;
traj_cloud.header.stamp = ros::Time::now();
sensor_msgs::PointCloud2Modifier cloud_mod(traj_cloud);
cloud_mod.setPointCloud2Fields(5, "x", 1, sensor_msgs::PointField::FLOAT32,
"y", 1, sensor_msgs::PointField::FLOAT32,
"z", 1, sensor_msgs::PointField::FLOAT32,
"theta", 1, sensor_msgs::PointField::FLOAT32,
"cost", 1, sensor_msgs::PointField::FLOAT32);
unsigned int num_points = 0;
for(std::vector<base_local_planner::Trajectory>::iterator t=all_explored.begin(); t != all_explored.end(); ++t)
{
if (t->cost_<0)
continue;
num_points += t->getPointsSize();
}
cloud_mod.resize(num_points);
sensor_msgs::PointCloud2Iterator<float> iter_x(traj_cloud, "x");
for(std::vector<base_local_planner::Trajectory>::iterator t=all_explored.begin(); t != all_explored.end(); ++t)
{
if(t->cost_<0)
continue;
// Fill out the plan
for(unsigned int i = 0; i < t->getPointsSize(); ++i) {
double p_x, p_y, p_th;
t->getPoint(i, p_x, p_y, p_th);
iter_x[0] = p_x;
iter_x[1] = p_y;
iter_x[2] = 0.0;
iter_x[3] = p_th;
iter_x[4] = t->cost_;
++iter_x;
}
}
traj_cloud_pub_.publish(traj_cloud);
}
// verbose publishing of point clouds
if (publish_cost_grid_pc_) {
//we'll publish the visualization of the costs to rviz before returning our best trajectory
map_viz_.publishCostCloud(planner_util_->getCostmap());
}
// debrief stateful scoring functions
oscillation_costs_.updateOscillationFlags(pos, &result_traj_, planner_util_->getCurrentLimits().min_vel_trans);
//if we don't have a legal trajectory, we'll just command zero
if (result_traj_.cost_ < 0) {
drive_velocities.pose.position.x = 0;
drive_velocities.pose.position.y = 0;
drive_velocities.pose.position.z = 0;
drive_velocities.pose.orientation.w = 1;
drive_velocities.pose.orientation.x = 0;
drive_velocities.pose.orientation.y = 0;
drive_velocities.pose.orientation.z = 0;
} else {
drive_velocities.pose.position.x = result_traj_.xv_;
drive_velocities.pose.position.y = result_traj_.yv_;
drive_velocities.pose.position.z = 0;
tf2::Quaternion q;
q.setRPY(0, 0, result_traj_.thetav_);
tf2::convert(q, drive_velocities.pose.orientation);
}
return result_traj_;
}
};
7.fake_localization
8.global_planner
9.map_server
9. 1basic knowledge
1）map.yaml
CSDN Reference
image: /home/rock/map.pgm
resolution: 0.050000
origin: [-8.000000, -9.500000, 0.000000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
Required fields:
- image : Path to the image file containing the occupancy data; can be absolute, or relative to the location of the YAML file
- resolution : Resolution of the map, meters / pixel
- origin : The 2-D pose of the lower-left pixel in the map, as (x, y, yaw), with yaw as counterclockwise rotation (yaw=0 means no rotation). Many parts of the system currently ignore yaw.
- occupied_thresh : Pixels with occupancy probability greater than this threshold are considered completely occupied.
- free_thresh : Pixels with occupancy probability less than this threshold are considered completely free.
- negate : Whether the white/black free/occupied semantics should be reversed (interpretation of thresholds is unaffected)
Optional parameter:
- mode : Can have one of three values: trinary, scale, or raw. Trinary is the default. More information on how this changes the value interpretation is in the next section.
9.2include
9.3scripts
9.4src
image_loader.cpp
Map mode definition
**Map mode
* Default: TRINARY -
* value >= occ_th - Occupied (100)
* value <= free_th - Free (0)
* otherwise - Unknown
* SCALE -
* alpha < 1.0 - Unknown
* value >= occ_th - Occupied (100)
* value <= free_th - Free (0)
* otherwise - f( (free_th, occ_th) ) = (0, 100)
* (linearly map in between values to (0,100)
* RAW -
* value = value
The meaning of each parameter
* @param resp The map wil be written into here
* @param fname The image file to read from
* @param res The resolution of the map (gets stored in resp)
* @param negate If true, then whiter pixels are occupied, and blacker pixels are free
* @param occ_th Threshold above which pixels are occupied
* @param free_th Threshold below which pixels are free
* @param origin Triple specifying 2-D pose of lower-left corner of image
* @param mode Map mode
* @throws std::runtime_error If the image file can't be loaded
main.cpp
Fuctions:
1
try{}
catch(){}
code in cpp file
try {
doc["resolution"] >> res;
} catch (YAML::InvalidScalar &) {
ROS_ERROR("The map does not contain a resolution tag or it is invalid.");
exit(-1);
}
1
try---catch------finally
1)将预见可能引发异常的代码包含在try语句块中。
2)如果发生了异常，则转入catch的执行。catch有几种写法：
catch
这将捕获任何发生的异常。
catch(Exception e)
这将捕获任何发生的异常。另外，还提供e参数，你可以在处理异常时使用e参数来获得有关异常的信息。
catch(Exception的派生类 e)
这将捕获派生类定义的异常，例如，我想捕获一个无效操作的异常，可以如下写：
catch(InvalidOperationException e)
{
....
}
这样，如果try语句块中抛出的异常是InvalidOperationException，将转入该处执行，其他异常不处理
finally可以没有，也可以只有一个。无论有没有发生异常，它总会在这个异常处理结构的最后运行。即使你在try块内用return返回了，在返
回前，finally总是要执行，这以便让你有机会能够在异常处理最后做一些清理工作。如关闭数据库连接等等。
注意：如果没有catch语句块，那么finally块就是必须的。如果你不希望在这里处理异常，而当异常发生时提交到上层处理，但在这个地方
无论发生异常，都要必须要执行一些操作，就可以使用try finally.
2
try {
doc["image"] >> mapfname;
// TODO: make this path-handling more robust
if(mapfname.size() == 0)
{
ROS_ERROR("The image tag cannot be an empty string.");
exit(-1);
}
2
//TODO:xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
代表代码有待实现的功能，这段代码有待使得路径处理更加robust.
TODO: + 说明：
如果代码中有该标识，说明在标识处有功能代码待编写，待实现的功能在说明中会简略说明。
FIXME: + 说明：
如果代码中有该标识，说明标识处代码需要修正，甚至代码是错误的，不能工作，需要修复，如何修正会在说明中简略说明。
XXX: + 说明：
如果代码中有该标识，说明标识处代码虽然实现了功能，但是实现的方法有待商榷，希望将来能改进，要改进的地方会在说明中简略说明。
3
ros::init(argc, argv, "map_server", ros::init_options::AnonymousName);
3
ROS匿名节点，ROS节点应具有唯一的名称。 如果您在终端中运行一个节点，然后尝试在另一个终端上运行具有相同名称的另一个节点，则第一
个节点将被杀死，因此第二个节点可以启动。 总而言之，您将只能同时运行一个具有相同名称的节点。匿名节点可让您摆脱此规则。 创建匿名
节点时，将在节点名称中添加一个随机的唯一ID。 因此，现在您可以运行任意数量的节点。
4
double res = (argc == 2) ? 0.0 : atof(argv[2]);
4 三元运算符，Exp1 ? Exp2 : Exp3;
其中，Exp1、Exp2 和 Exp3 是表达式。请注意冒号的使用和位置。? : 表达式的值取决于 Exp1 的计算结果。如果 Exp1 为真，则计算
Exp2 的值，且 Exp2 的计算结果则为整个 ? : 表达式的值。如果 Exp1 为假，则计算 Exp3 的值，且 Exp3 的计算结果则为
整个 ? : 表达式的值。
map_saver.cpp
Fuctions:
1
int ret = strcmp(str1,str2);
code in .cpp file:
if(!strcmp(argv[i], "-h"))
{
puts(USAGE);
return 0;
}
1
strcmp函数接受接受两个字符串类型的参数传入，根据比较结果返回一个int类型的值。
若返回结果的值小于 0，则表示在ASCII码上， str1字符串 小于 str2字符串。
返回结果返回值 大于0，则代表 str2字符串 小于 str1字符串。
只有当返回值 为0时，两个字符串才是相同的。
map_server.dox
9.5test
CHANHELOG.rst
CMakeLists.txt
package.xml
10.move_base
11.move_slow_and_clear
12.nav_core
base_local_planner
computeVelocityCommands
Given the current position ,orientation, and velocity of the robot，compute velocity commands to send to the base.
isGoalReached
Check if the goal pose has been achieved by the local planner
setPlan
Set the plan that the local planner is following
Initialize
Constructs the local planner
13.navfn
14navigation
15.rotate_recovery
16.voxel_grid
CMakeLists.txt
README.md
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