导航框架及机器人自主导航

导航框架

move_base

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安装：sudo apt-get install ros-kinetic-navigation

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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
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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

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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
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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：
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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：
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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：
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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：
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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

duganlx

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