導航框架
move_base
安裝:sudo apt-get install ros-kinetic-navigation
全局路徑規劃(global planner)
- 全局最優路徑規劃
- Dijkstra或A* 算法
本地實時規劃(local planner)
- 規劃機器人每個週期內的線速度、角速度,使之儘量符合全局最優路徑
- 實時避障
- Trajectory Rollout 和 Dynamic Window Approaches算法
- 搜索躲避和行進的多條路徑,綜合各評價標準選取最優路徑
move_base功能包中的話題和服務
配置move_base節點:
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
總用量 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
裏面的參數含義參考:http://wiki.ros.org/move_base
base_local_planner_params.yaml
該配置文件裏面配置了局部路徑規劃相關的配置
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
該配置文件裏面配置了代價地圖共用的參數
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
該配置文件裏面配置了全局代價地圖相關參數
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
該配置文件裏面配置了局部代價地圖共用的參數
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
- 蒙特卡羅定位方法
- 二維環境定位
- 針對已有地圖使用粒子濾波器跟蹤一個機器人的姿態
amcl功能包中的話題和服務
里程計定位:只通過里程計的數據來處理/base
和/odom
之間的tf轉換
amcl定位:可以估算機器人在地圖座標系/map
下的位姿信息,提供/base
、/odom
、/map
之間的tf變換
配置amcl節點
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>
機器人自主導航
導航示例(《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:
運行roslaunch rbx1_bringup fake_turtlebot.launch
時,如果出錯的話,運行一下命令:
sudo apt-get install ros-kinetic-arbotix-*
正常運行的話,機器人會走過指定的四個點,在運動的過程中,自主進行路徑規劃和避障
導航仿真
roslaunch mbot_gazebo mbot_laser_nav_gazebo.launch
roslaunch mbot_navigation nav_cloister_demo.launch
效果如下:
在機器人導航的過程中,在gazebo中給出臨時障礙物,機器人可以順利避開
導航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:
代碼分析:
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)
# 在每個目標位置暫停的時間 (單位:s)
self.rest_time = rospy.get_param("~rest_time", 2)
# 是否仿真?
self.fake_test = rospy.get_param("~fake_test", True)
# 到達目標的狀態
goal_states = ['PENDING', 'ACTIVE', 'PREEMPTED',
'SUCCEEDED', 'ABORTED', 'REJECTED',
'PREEMPTING', 'RECALLING', 'RECALLED',
'LOST']
# 設置目標點的位置
# 在rviz中點擊 2D Nav Goal 按鍵,然後單擊地圖中一點
# 在終端中就會看到該點的座標信息
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))
# 發佈控制機器人的消息
self.cmd_vel_pub = rospy.Publisher('cmd_vel', Twist, queue_size=5)
# 訂閱move_base服務器的消息
self.move_base = actionlib.SimpleActionClient("move_base", MoveBaseAction)
rospy.loginfo("Waiting for move_base action server...")
# 60s等待時間限制
self.move_base.wait_for_server(rospy.Duration(60))
rospy.loginfo("Connected to move base server")
# 保存機器人的在rviz中的初始位置
initial_pose = PoseWithCovarianceStamped()
# 保存成功率、運行時間、和距離的變量
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 = ""
# 確保有初始位置
while initial_pose.header.stamp == "":
rospy.sleep(1)
rospy.loginfo("Starting navigation test")
# 開始主循環,隨機導航
while not rospy.is_shutdown():
# 如果已經走完了所有點,再重新開始排序
if i == n_locations:
i = 0
sequence = sample(locations, n_locations)
# 如果最後一個點和第一個點相同,則跳過
if sequence[0] == last_location:
i = 1
# 在當前的排序中獲取下一個目標點
location = sequence[i]
# 跟蹤行駛距離
# 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 = ""
# 存儲上一次的位置,計算距離
last_location = location
# 計數器加1
i += 1
n_goals += 1
# 設定下一個目標點
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()
# 讓用戶知道下一個位置
rospy.loginfo("Going to: " + str(location))
# 向下一個位置進發
self.move_base.send_goal(self.goal)
# 五分鐘時間限制
finished_within_time = self.move_base.wait_for_result(rospy.Duration(300))
# 查看是否成功到達
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]))
# 運行所用時間
running_time = rospy.Time.now() - start_time
running_time = running_time.secs / 60.0
# 輸出本次導航的所有信息
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.")
小結
機器人必備條件
- 硬件要求:差分輪式、速度控制指令、深度信息、外觀圓形或方形
- 里程計信息:獲取仿真機器人/真實機器人的實時位置、速度
- 仿真環境:構建仿真環境,爲後續SLAM、導航仿真作準備
ROS SLAM功能包應用方法
- gmapping
- 輸入:激光雷達、里程計信息
- 輸出:二維柵格地圖
- hector_slam
- 輸入: 輸入激光雷達信息
- 輸出:二維柵格地圖
- cartographer
- 輸入:激光雷達信息
- 輸出:二維或三維地圖
- orb_slam
- 輸入:單目攝像頭信息
- 輸出:三維點雲地圖
ROS的導航框架
- move_base:全局規劃和局部規劃
- amcl:二維概率定位
ROS機器人自主導航
- rviz+Arbotix的功能仿真
- gazebo環境下自主導航的仿真
- 導航過程中同步slam建圖