ros2 拉 humble 镜像 arm

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桃太郎 2025-06-27 19:04:31

文章标签 机器人 2d 路径规划移动机器人 文章分类 云原生云计算

由于我们已经成功保存了SLAM建图的地图，后面就能在这个地图进行导航

ros实现导航可以在move base和amcl两个功能包基础上完成，首先我们使用amcl，

AMCL(adaptive Monte Carlo Localization) 是用于2D移动机器人的概率定位系统，它实现了自适应（或KLD采样）蒙特卡洛定位方法，可以根据已有地图使用粒子滤波器推算机器人位置。

amcl已经被集成到了navigation包，navigation安装前面也有介绍，命令如下:

sudo apt install ros-<ROS版本>-navigation

关于launch文件的实现，在amcl功能包下的example目录已经给出了示例，可以作为参考，具体实现:

roscd amcl
ls examples

该目录下会列出两个文件: amcl_diff.launch 和 amcl_omni.launch 文件，前者适用于差分移动机器人，后者适用于全向移动机器人，可以按需选择，此处参考前者，新建 launch 文件，复制 amcl.launch 文件内容并修改如下:

<launch>
<node pkg="amcl" type="amcl" name="amcl" output="screen">
  <!-- Publish scans from best pose at a max of 10 Hz -->
  <param name="odom_model_type" value="diff"/>
  <param name="odom_alpha5" value="0.1"/>
  <param name="transform_tolerance" value="0.2" />
  <param name="gui_publish_rate" value="10.0"/>
  <param name="laser_max_beams" value="30"/>
  <param name="min_particles" value="500"/>
  <param name="max_particles" value="5000"/>
  <param name="kld_err" value="0.01"/>
  <param name="kld_z" value="0.5"/>
  <param name="odom_alpha1" value="0.005"/>
  <param name="odom_alpha2" value="0.005"/>
  <!-- translation std dev, m -->
  <param name="odom_alpha3" value="0.005"/>
  <param name="odom_alpha4" value="0.005"/>
  <param name="laser_z_hit" value="0.9"/>
  <param name="laser_z_short" value="0.05"/>
  <param name="laser_z_max" value="0.05"/>
  <param name="laser_z_rand" value="0.5"/>
  <param name="laser_sigma_hit" value="0.1"/>
  <param name="laser_lambda_short" value="0.1"/>
  <param name="laser_lambda_short" value="0.1"/>
  <param name="laser_model_type" value="likelihood_field"/>
  <!-- <param name="laser_model_type" value="beam"/> -->
  <param name="laser_likelihood_max_dist" value="4.0"/>
  <param name="update_min_d" value="0.2"/>
  <param name="update_min_a" value="0.5"/>

  <param name="odom_frame_id" value="odom"/>
  <param name="base_frame_id" value="base_footprint"/>
  <param name="global_frame_id" value="map"/>

  <param name="resample_interval" value="1"/>
  <param name="transform_tolerance" value="0.1"/>
  <param name="recovery_alpha_slow" value="0.0"/>
  <param name="recovery_alpha_fast" value="0.0"/>
</node>
</launch>

上面完成了amcl定位编写，接下来使用move base集成A*和DWA算法进行路径规划

move_base已经被集成到了navigation包，navigation安装前面也有介绍，命令如下:

sudo apt install ros-<ROS版本>-navigation

然后就要编写代价地图

我现在就描述一下我的编写过程

我们需要定义地图的参数，机器人具体避障信息等等，具体目录如下

ros2 拉 humble 镜像 arm_2d

（我已经使用DWA作为local planner了，base local planner在后面不会继续使用，这里只是没删掉）

costmap_common_params.yaml
该文件是move_base 在全局路径规划与本地路径规划时调用的通用参数，包括:机器人的尺寸、距离障碍物的安全距离、传感器信息等。配置参考如下:

footprint: [[-0.12, -0.12], [-0.12, 0.12], [0.12, 0.12], [0.12, -0.12]] 

footprint_padding: 0.00 
footprint_inflation: 0.025 # 机器人膨胀层 
transform_tolerance: 2.0 
always_send_full_costmap: false #default: false 
obstacle_layer: 
  enabled: true 
  obstacle_range: 5.0 # 规避障碍的范围 
  raytrace_range: 5.5 # 探测障碍的范围 
  inflation_radius: 0.2 
  track_unknown_space: true 
  combination_method: 1 
  observation_sources: laser_scan_sensor 
  laser_scan_sensor: {data_type: LaserScan, topic: scan, marking: true, clearing: true} 


static_layer: 
  enabled: true 
  map_topic: "map"
dwa_local_planner_params.yaml
DWAPlannerROS:

# Robot Configuration Parameters
  max_vel_x: 0.22
  min_vel_x: -0.22

  max_vel_y: 0.0
  min_vel_y: 0.0

# The velocity when robot is moving in a straight line
  max_vel_trans:  0.22
  min_vel_trans:  0.11

  max_vel_theta: 2.75
  min_vel_theta: 1.37

  acc_lim_x: 2.5
  acc_lim_y: 0.0
  acc_lim_theta: 3.2 

# Goal Tolerance Parameters
  xy_goal_tolerance: 0.05
  yaw_goal_tolerance: 0.17
  latch_xy_goal_tolerance: false

# Forward Simulation Parameters
  sim_time: 1.5
  vx_samples: 20
  vy_samples: 0
  vth_samples: 40
  controller_frequency: 10.0

# Trajectory Scoring Parameters
  path_distance_bias: 32.0
  goal_distance_bias: 20.0
  occdist_scale: 0.02
  forward_point_distance: 0.325
  stop_time_buffer: 0.2
  scaling_speed: 0.25
  max_scaling_factor: 0.2

# Oscillation Prevention Parameters
  oscillation_reset_dist: 0.05

# Debugging
  publish_traj_pc : true
  publish_cost_grid_pc: true
global_costmap_params.yaml

该文件用于全局代价地图参数设置:

global_costmap:
  global_frame: map 
  robot_base_frame: base_footprint 

  update_frequency: 1.0 
  publish_frequency: 1.0 
  transform_tolerance: 0.5 

  static_map: true
  plugins: 
    - {name: static_layer, type: "costmap_2d::StaticLayer"} 
    - {name: obstacle_layer, type: "costmap_2d::ObstacleLayer"} 
    - {name: inflation_layer, type: "costmap_2d::InflationLayer"}

  inflation_layer: 
    enabled: true 
    cost_scaling_factor: 5.0 # exponential rate at which the obstacle cost drops off (default: 10) 
    inflation_radius: 0.5 # max. distance from an obstacle at which costs are incurred for planning paths.
local_costmap_params.yaml

该文件用于局部代价地图参数设置:

local_costmap:
  global_frame: odom 
  robot_base_frame: base_footprint 

  update_frequency: 10.0 
  publish_frequency: 10.0 
  transform_tolerance: 0.5 

  static_map: false  
  rolling_window: true 
  width: 3 
  height: 3 
  resolution: 0.05

  plugins: 
    - {name: obstacle_layer, type: "costmap_2d::ObstacleLayer"} 
    - {name: inflation_layer, type: "costmap_2d::InflationLayer"}

  inflation_layer:
    enabled: true 
    cost_scaling_factor: 10.0 # exponential rate at which the obstacle cost drops off (default: 10) 
    inflation_radius: 0.05
move_base_params.yaml
shutdown_costmaps: false
controller_frequency: 10.0
planner_patience: 5.0
controller_patience: 15.0
conservative_reset_dist: 3.0
planner_frequency: 5.0
oscillation_timeout: 10.0
oscillation_distance: 0.2
base_global_planner: "astar_planner/AstarPlanner"
base_local_planner: "dwa_local_planner/DWAPlannerROS"

关于move_base节点的调用，代码如下(文件名为path）

<launch>

    <node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen" clear_params="true">
        <rosparam file="$(find nav)/param/costmap_common_params.yaml" command="load" ns="global_costmap" />
        <rosparam file="$(find nav)/param/costmap_common_params.yaml" command="load" ns="local_costmap" />
        <rosparam file="$(find nav)/param/local_costmap_params.yaml" command="load" />
        <rosparam file="$(find nav)/param/global_costmap_params.yaml" command="load" />
        <!--<rosparam file="$(find nav)/param/base_local_planner_params.yaml" command="load" /> -->
       <rosparam file="$(find nav)/param/move_base_params.yaml" command="load" />-
      <rosparam file="$(find nav)/param/dwa_local_planner_params.yaml" command="load" /> 
    </node>

</launch>

如果要实现导航，需要集成地图服务、amcl 、move_base 与 Rviz 等，集成示例如下:

<launch>
    <!-- 设置地图的配置文件 -->
    <arg name="map" default="nav.yaml" />
    <!-- 运行地图服务器，并且加载设置的地图-->
    <node name="map_server" pkg="map_server" type="map_server" args="$(find nav)/map/$(arg map)"/>
    <!-- 启动AMCL节点 -->
    <include file="$(find nav)/launch/amcl.launch" />

    <!-- 运行move_base节点 -->
    <include file="$(find nav)/launch/path.launch" />
    <!-- 运行rviz -->
    <node pkg="rviz" type="rviz" name="rviz"/>

</launch>

打开之后，我们加入全局和局部代价地图，加入A*算法和DWA算法，就可以全局和局部路径规划同时使用了，效果在B站，性能我还不确定是不是很好

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