概述
导航框架
move_base
安装：sudo apt-get install ros-kinetic-navigation
全局路径规划（global planner）
- 全局最优路径规划
- Dijkstra或A* 算法
本地实时规划（local planner）
- 规划机器人每个周期内的线速度、角速度，使之尽量符合全局最优路径
- 实时避障
- Trajectory Rollout 和 Dynamic Window Approaches算法
- 搜索躲避和行进的多条路径，综合各评价标准选取最优路径
move_base功能包中的话题和服务
配置move_base节点：
src5/mbot_navigation/launch/move_base.launch
<launch>
<node pkg="move_base" type="move_base" respawn="false" name="move_base" output="screen" clear_params="true">
<rosparam file="$(find mbot_navigation)/config/mbot/costmap_common_params.yaml" command="load" ns="global_costmap" />
<rosparam file="$(find mbot_navigation)/config/mbot/costmap_common_params.yaml" command="load" ns="local_costmap" />
<rosparam file="$(find mbot_navigation)/config/mbot/local_costmap_params.yaml" command="load" />
<rosparam file="$(find mbot_navigation)/config/mbot/global_costmap_params.yaml" command="load" />
<rosparam file="$(find mbot_navigation)/config/mbot/base_local_planner_params.yaml" command="load" />
</node>
</launch>
其中所引用的文件在src5/mbot_navigation/config/mbot
总用量 16
-rw-rw-r-- 1 ddu ddu 1027 4月 26 2018 base_local_planner_params.yaml
-rw-rw-r-- 1 ddu ddu 364 4月 26 2018 costmap_common_params.yaml
-rw-rw-r-- 1 ddu ddu 238 4月 26 2018 global_costmap_params.yaml
-rw-rw-r-- 1 ddu ddu 246 4月 26 2018 local_costmap_params.yaml
里面的参数含义参考：http://wiki.ros.org/move_base
base_local_planner_params.yaml
该配置文件里面配置了局部路径规划相关的配置
controller_frequency: 3.0
recovery_behavior_enabled: false
clearing_rotation_allowed: false
TrajectoryPlannerROS:
max_vel_x: 0.5
min_vel_x: 0.1
max_vel_y: 0.0 # zero for a differential drive robot
min_vel_y: 0.0
max_vel_theta: 1.0
min_vel_theta: -1.0
min_in_place_vel_theta: 0.5
escape_vel: -0.1
acc_lim_x: 1.5
acc_lim_y: 0.0 # zero for a differential drive robot
acc_lim_theta: 1.2
holonomic_robot: false
yaw_goal_tolerance: 0.1 # about 6 degrees
xy_goal_tolerance: 0.1 # 10 cm
latch_xy_goal_tolerance: false
pdist_scale: 0.9
gdist_scale: 0.6
meter_scoring: true
heading_lookahead: 0.325
heading_scoring: false
heading_scoring_timestep: 0.8
occdist_scale: 0.1
oscillation_reset_dist: 0.05
publish_cost_grid_pc: false
prune_plan: true
sim_time: 1.0
sim_granularity: 0.025
angular_sim_granularity: 0.025
vx_samples: 8
vy_samples: 0 # zero for a differential drive robot
vtheta_samples: 20
dwa: true
simple_attractor: false
costmap_common_params.yaml
该配置文件里面配置了代价地图共用的参数
obstacle_range: 2.5
raytrace_range: 3.0
footprint: [[0.175, 0.175], [0.175, -0.175], [-0.175, -0.175], [-0.175, 0.175]]
footprint_inflation: 0.01
robot_radius: 0.175
inflation_radius: 0.15
max_obstacle_height: 0.6
min_obstacle_height: 0.0
observation_sources: scan
scan: {data_type: LaserScan, topic: /scan, marking: true, clearing: true, expected_update_rate: 0}
global_costmap_params.yaml
该配置文件里面配置了全局代价地图相关参数
global_costmap:
global_frame: map
robot_base_frame: base_footprint
update_frequency: 1.0
publish_frequency: 1.0
static_map: true
rolling_window: false
resolution: 0.01
transform_tolerance: 1.0
map_type: costmap
local_costmap_params.yaml
该配置文件里面配置了局部代价地图共用的参数
local_costmap:
global_frame: odom
robot_base_frame: base_footprint
update_frequency: 3.0
publish_frequency: 1.0
static_map: true
rolling_window: false
width: 6.0
height: 6.0
resolution: 0.01
transform_tolerance: 1.0
amcl
- 蒙特卡罗定位方法
- 二维环境定位
- 针对已有地图使用粒子滤波器跟踪一个机器人的姿态
amcl功能包中的话题和服务
里程计定位：只通过里程计的数据来处理/base 和/odom之间的tf转换
amcl定位：可以估算机器人在地图坐标系/map下的位姿信息，提供/base、/odom、/map 之间的tf变换
配置amcl节点
src5/mbot_navigation/launch/amcl.launch
<launch>
<arg name="use_map_topic" default="false"/>
<arg name="scan_topic" default="scan"/>
<node pkg="amcl" type="amcl" name="amcl" clear_params="true">
<param name="use_map_topic" value="$(arg use_map_topic)"/>
<!-- 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="gui_publish_rate" value="10.0"/>
<param name="laser_max_beams" value="60"/>
<param name="laser_max_range" value="12.0"/>
<param name="min_particles" value="500"/>
<param name="max_particles" value="2000"/>
<param name="kld_err" value="0.05"/>
<param name="kld_z" value="0.99"/>
<param name="odom_alpha1" value="0.2"/>
<param name="odom_alpha2" value="0.2"/>
<!-- translation std dev, m -->
<param name="odom_alpha3" value="0.2"/>
<param name="odom_alpha4" value="0.2"/>
<param name="laser_z_hit" value="0.5"/>
<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="0.2"/>
<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="2.0"/>
<param name="update_min_d" value="0.25"/>
<param name="update_min_a" value="0.2"/>
<param name="odom_frame_id" value="odom"/>
<param name="resample_interval" value="1"/>
<!-- Increase tolerance because the computer can get quite busy -->
<param name="transform_tolerance" value="1.0"/>
<param name="recovery_alpha_slow" value="0.0"/>
<param name="recovery_alpha_fast" value="0.0"/>
<remap from="scan" to="$(arg scan_topic)"/>
</node>
</launch>
机器人自主导航
导航示例（《ROS by Example》）
roslaunch rbx1_bringup fake_turtlebot.launch
roslaunch rbx1_nav fake_move_base_map_with_obstacles.launch
rosrun rviz rviz -d `rospack find rbx1_nav`/nav_obstacles.rviz
rosrun rbx1_nav move_base_square.py
效果如下：
运行roslaunch rbx1_bringup fake_turtlebot.launch时，如果出错的话，运行一下命令：
sudo apt-get install ros-kinetic-arbotix-*
正常运行的话，机器人会走过指定的四个点，在运动的过程中，自主进行路径规划和避障
导航仿真
roslaunch mbot_gazebo mbot_laser_nav_gazebo.launch
roslaunch mbot_navigation nav_cloister_demo.launch
效果如下：
在机器人导航的过程中，在gazebo中给出临时障碍物，机器人可以顺利避开
导航slam仿真
roslaunch mbot_gazebo mbot_laser_nav_gazebo.launch
roslaunch mbot_navigation exploring_slam_demo.launch
效果如下：
自主探索slam仿真
roslaunch mbot_gazebo mbot_laser_nav_gazebo.launch
roslaunch mbot_navigation exploring_slam_demo.launch
rosrun mbot_navigation exploring_slam.py
效果展示：
代码分析：
src5/mbot_navigation/scripts/exploring_slam.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import roslib;
import rospy
import actionlib
from actionlib_msgs.msg import *
from geometry_msgs.msg import Pose, PoseWithCovarianceStamped, Point, Quaternion, Twist
from move_base_msgs.msg import MoveBaseAction, MoveBaseGoal
from random import sample
from math import pow, sqrt
class NavTest():
def __init__(self):
rospy.init_node('exploring_slam', anonymous=True)
rospy.on_shutdown(self.shutdown)
# 在每个目标位置暂停的时间 (单位：s)
self.rest_time = rospy.get_param("~rest_time", 2)
# 是否仿真？
self.fake_test = rospy.get_param("~fake_test", True)
# 到达目标的状态
goal_states = ['PENDING', 'ACTIVE', 'PREEMPTED',
'SUCCEEDED', 'ABORTED', 'REJECTED',
'PREEMPTING', 'RECALLING', 'RECALLED',
'LOST']
# 设置目标点的位置
# 在rviz中点击 2D Nav Goal 按键，然后单击地图中一点
# 在终端中就会看到该点的坐标信息
locations = dict()
locations['1'] = Pose(Point(4.589, -0.376, 0.000), Quaternion(0.000, 0.000, -0.447, 0.894))
locations['2'] = Pose(Point(4.231, -6.050, 0.000), Quaternion(0.000, 0.000, -0.847, 0.532))
locations['3'] = Pose(Point(-0.674, -5.244, 0.000), Quaternion(0.000, 0.000, 0.000, 1.000))
locations['4'] = Pose(Point(-5.543, -4.779, 0.000), Quaternion(0.000, 0.000, 0.645, 0.764))
locations['5'] = Pose(Point(-4.701, -0.590, 0.000), Quaternion(0.000, 0.000, 0.340, 0.940))
locations['6'] = Pose(Point(2.924, 0.018, 0.000), Quaternion(0.000, 0.000, 0.000, 1.000))
# 发布控制机器人的消息
self.cmd_vel_pub = rospy.Publisher('cmd_vel', Twist, queue_size=5)
# 订阅move_base服务器的消息
self.move_base = actionlib.SimpleActionClient("move_base", MoveBaseAction)
rospy.loginfo("Waiting for move_base action server...")
# 60s等待时间限制
self.move_base.wait_for_server(rospy.Duration(60))
rospy.loginfo("Connected to move base server")
# 保存机器人的在rviz中的初始位置
initial_pose = PoseWithCovarianceStamped()
# 保存成功率、运行时间、和距离的变量
n_locations = len(locations)
n_goals = 0
n_successes = 0
i = n_locations
distance_traveled = 0
start_time = rospy.Time.now()
running_time = 0
location = ""
last_location = ""
# 确保有初始位置
while initial_pose.header.stamp == "":
rospy.sleep(1)
rospy.loginfo("Starting navigation test")
# 开始主循环，随机导航
while not rospy.is_shutdown():
# 如果已经走完了所有点，再重新开始排序
if i == n_locations:
i = 0
sequence = sample(locations, n_locations)
# 如果最后一个点和第一个点相同，则跳过
if sequence[0] == last_location:
i = 1
# 在当前的排序中获取下一个目标点
location = sequence[i]
# 跟踪行驶距离
# 使用更新的初始位置
if initial_pose.header.stamp == "":
distance = sqrt(pow(locations[location].position.x -
locations[last_location].position.x, 2) +
pow(locations[location].position.y -
locations[last_location].position.y, 2))
else:
rospy.loginfo("Updating current pose.")
distance = sqrt(pow(locations[location].position.x -
initial_pose.pose.pose.position.x, 2) +
pow(locations[location].position.y -
initial_pose.pose.pose.position.y, 2))
initial_pose.header.stamp = ""
# 存储上一次的位置，计算距离
last_location = location
# 计数器加1
i += 1
n_goals += 1
# 设定下一个目标点
self.goal = MoveBaseGoal()
self.goal.target_pose.pose = locations[location]
self.goal.target_pose.header.frame_id = 'map'
self.goal.target_pose.header.stamp = rospy.Time.now()
# 让用户知道下一个位置
rospy.loginfo("Going to: " + str(location))
# 向下一个位置进发
self.move_base.send_goal(self.goal)
# 五分钟时间限制
finished_within_time = self.move_base.wait_for_result(rospy.Duration(300))
# 查看是否成功到达
if not finished_within_time:
self.move_base.cancel_goal()
rospy.loginfo("Timed out achieving goal")
else:
state = self.move_base.get_state()
if state == GoalStatus.SUCCEEDED:
rospy.loginfo("Goal succeeded!")
n_successes += 1
distance_traveled += distance
rospy.loginfo("State:" + str(state))
else:
rospy.loginfo("Goal failed with error code: " + str(goal_states[state]))
# 运行所用时间
running_time = rospy.Time.now() - start_time
running_time = running_time.secs / 60.0
# 输出本次导航的所有信息
rospy.loginfo("Success so far: " + str(n_successes) + "/" +
str(n_goals) + " = " +
str(100 * n_successes/n_goals) + "%")
rospy.loginfo("Running time: " + str(trunc(running_time, 1)) +
" min Distance: " + str(trunc(distance_traveled, 1)) + " m")
rospy.sleep(self.rest_time)
def update_initial_pose(self, initial_pose):
self.initial_pose = initial_pose
def shutdown(self):
rospy.loginfo("Stopping the robot...")
self.move_base.cancel_goal()
rospy.sleep(2)
self.cmd_vel_pub.publish(Twist())
rospy.sleep(1)
def trunc(f, n):
slen = len('%.*f' % (n, f))
return float(str(f)[:slen])
if __name__ == '__main__':
try:
NavTest()
rospy.spin()
except rospy.ROSInterruptException:
rospy.loginfo("Exploring SLAM finished.")
小结
机器人必备条件
- 硬件要求：差分轮式、速度控制指令、深度信息、外观圆形或方形
- 里程计信息：获取仿真机器人/真实机器人的实时位置、速度
- 仿真环境：构建仿真环境，为后续SLAM、导航仿真作准备
ROS SLAM功能包应用方法
- gmapping
- 输入：激光雷达、里程计信息
- 输出：二维栅格地图
- hector_slam
- 输入： 输入激光雷达信息
- 输出：二维栅格地图
- cartographer
- 输入：激光雷达信息
- 输出：二维或三维地图
- orb_slam
- 输入：单目摄像头信息
- 输出：三维点云地图
ROS的导航框架
- move_base：全局规划和局部规划
- amcl：二维概率定位
ROS机器人自主导航
- rviz+Arbotix的功能仿真
- gazebo环境下自主导航的仿真
- 导航过程中同步slam建图
最后
以上就是曾经吐司为你收集整理的导航框架及机器人自主导航的全部内容，希望文章能够帮你解决导航框架及机器人自主导航所遇到的程序开发问题。
如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。
本图文内容来源于网友提供，作为学习参考使用，或来自网络收集整理，版权属于原作者所有。
发表评论 取消回复