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ROS探索总结(十)——语音控制

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如今语音识别在PC机和智能手机上炒的火热,ROS走在技术的最前沿当然也不会错过这么帅的技术。ROS中使用了CMU Sphinx和Festival开源项目中的代码,发布了独立的语音识别包,而且可以将识别出来的语音转换成文字,然后让机器人智能处理后说话。

一、语音识别包

1、安装

        安装很简单,直接使用ubuntu命令即可,首先安装依赖库:

  1. $ sudo apt-get install gstreamer0.10-pocketsphinx
  2. $ sudo apt-get install ros-fuerte-audio-common
  3. $ sudo apt-get install libasound2

        然后来安装ROS包:

  1. $ svn checkout http://albany-ros-pkg.googlecode.com/svn/trunk/rharmony
  2. $ rosmake pocketsphinx
        其中的核心文件就是nodes文件夹下的recognizer.py文件了。这个文件通过麦克风收集语音信息,然后调用语音识别库进行识别生成文本信息,通过/recognizer/output消息发布,其他节点就可以订阅该消息然后进行相应的处理了。

2、测试

        安装完成后我们就可以运行测试了。
首先,插入你的麦克风设备,然后在系统设置里测试麦克风是否有语音输入。
然后,运行包中的测试程序:$ roslaunch pocketsphinx robocup.launch
        此时,在终端中会看到一大段的信息。尝试说一些简单的语句,当然,必须是英语,例如:bring me the glass,come with me,看看能不能识别出来。《ros by example》这本书中写得测试还是很准确的,但是我在测试中感觉识别相当不准确,可能是我的英语太差了吧。

        我们也可以直接看ROS最后发布的结果消息:$ rostopic echo /recognizer/output

二、语音库

1、查看语音库

        这个语音识别时一种离线识别的方法,将一些常用的词汇放到一个文件中,作为识别的文本库,然后分段识别语音信号,最后在库中搜索对应的文本信息。如果想看语音识别库中有哪些文本信息,可以通过下面的指令进行查询:

  1. $ roscd pocketsphinx/demo
  2. $ more robocup.corpus

2、添加语音库

        我们可以自己向语音库中添加其他的文本识别信息,《ros by example》自带的例程中是带有语音识别的例程的,而且有添加语音库的例子。
首先看看例子中要添加的文本信息:

  1. $ roscd rbx1_speech/config
  2. $ more nav_commands.txt
        这就是需要添加的文本,我们也可以修改其中的某些文本,改成自己需要的。
然后我们要把这个文件在线生成语音信息和库文件,这一步需要登陆网站http://www.speech.cs.cmu.edu/tools/lmtool-new.html,根据网站的提示上传文件,然后在线编译生成库文件。

        把下载的文件都解压放在rbx1_speech包的config文件夹下。我们可以给这些文件改个名字:

  1. $ roscd rbx1_speech/config
  2. $ rename -f ‘s/3026/nav_commands/’ *

        在rbx1_speech/launch文件夹下看看voice_nav_commands.launch这个文件:

  1. <launch>
  2. <node name=”recognizer” pkg=”pocketsphinx” type=”recognizer.py”
  3. output=”screen”>
  4. <param name=”lm” value=”$(find rbx1_speech)/config/nav_commands.lm”/>
  5. <param name=”dict” value=”$(find rbx1_speech)/config/nav_commands.dic”/>
  6. </node>
  7. </launch>
        可以看到,这个launch文件在运行recognizer.py节点的时候使用了我们生成的语音识别库和文件参数,这样就可以实用我们自己的语音库来进行语音识别了。
通过之前的命令来测试一下效果如何吧:

  1. $ roslaunch rbx1_speech voice_nav_commands.launch
  2. $ rostopic echo /recognizer/output

三、语音控制

        有了语音识别,我们就可以来做很多犀利的应用了,首先先来尝试一下用语音来控制机器人动作。

1、机器人控制节点

        前面说到的recognizer.py会将最后识别的文本信息通过消息发布,那么我们来编写一个机器人控制节点接收这个消息,进行相应的控制即可。
在pocketsphinx包中本身有一个语音控制发布Twist消息的例程voice_cmd_vel.py,rbx1_speech包对其进行了一些简化修改,在nodes文件夹里可以查看voice_nav.py文件:
 #!/usr/bin/env python
"""
  voice_nav.py  
  Allows controlling a mobile base using simple speech commands. 
  Based on the voice_cmd_vel.py script by Michael Ferguson in
  the pocketsphinx ROS package.  
  See http://www.ros.org/wiki/pocketsphinx
"""
import roslib; roslib.load_manifest('rbx1_speech')
import rospy
from geometry_msgs.msg import Twist
from std_msgs.msg import String
from math import copysign
class VoiceNav:
    def __init__(self):
        rospy.init_node('voice_nav')   
        rospy.on_shutdown(self.cleanup)    
        # Set a number of parameters affecting the robot's speed
        self.max_speed = rospy.get_param("~max_speed", 0.4)
        self.max_angular_speed = rospy.get_param("~max_angular_speed", 1.5)
        self.speed = rospy.get_param("~start_speed", 0.1)
        self.angular_speed = rospy.get_param("~start_angular_speed", 0.5)
        self.linear_increment = rospy.get_param("~linear_increment", 0.05)
        self.angular_increment = rospy.get_param("~angular_increment", 0.4)
        
        # We don't have to run the script very fast
        self.rate = rospy.get_param("~rate", 5)
        r = rospy.Rate(self.rate)
        
        # A flag to determine whether or not voice control is paused
        self.paused = False
        
        # Initialize the Twist message we will publish.
        self.cmd_vel = Twist()

        # Publish the Twist message to the cmd_vel topic
        self.cmd_vel_pub = rospy.Publisher('cmd_vel', Twist)
        
        # Subscribe to the /recognizer/output topic to receive voice commands.
        rospy.Subscriber('/recognizer/output', String, self.speech_callback)
        
        # A mapping from keywords or phrases to commands
        self.keywords_to_command = {'stop': ['stop', 'halt', 'abort', 'kill', 'panic', 'off', 'freeze', 'shut down', 'turn off', 'help', 'help me'],
                                    'slower': ['slow down', 'slower'],
                                    'faster': ['speed up', 'faster'],
                                    'forward': ['forward', 'ahead', 'straight'],
                                    'backward': ['back', 'backward', 'back up'],
                                    'rotate left': ['rotate left'],
                                    'rotate right': ['rotate right'],
                                    'turn left': ['turn left'],
                                    'turn right': ['turn right'],
                                    'quarter': ['quarter speed'],
                                    'half': ['half speed'],
                                    'full': ['full speed'],
                                    'pause': ['pause speech'],
                                    'continue': ['continue speech']}  
        rospy.loginfo("Ready to receive voice commands")
        
        # We have to keep publishing the cmd_vel message if we want the robot to keep moving.
        while not rospy.is_shutdown():
            self.cmd_vel_pub.publish(self.cmd_vel)
            r.sleep()                       
    def get_command(self, data):
        # Attempt to match the recognized word or phrase to the 
        # keywords_to_command dictionary and return the appropriate
        # command
        for (command, keywords) in self.keywords_to_command.iteritems():
            for word in keywords:
                if data.find(word) > -1:
                    return command
        
    def speech_callback(self, msg):
        # Get the motion command from the recognized phrase
        command = self.get_command(msg.data)
        
        # Log the command to the screen
        rospy.loginfo("Command: " + str(command))
        
        # If the user has asked to pause/continue voice control,
        # set the flag accordingly 
        if command == 'pause':
            self.paused = True
        elif command == 'continue':
            self.paused = False
        
        # If voice control is paused, simply return without
        # performing any action
        if self.paused:
            return       
        
        # The list of if-then statements should be fairly
        # self-explanatory
        if command == 'forward':    
            self.cmd_vel.linear.x = self.speed
            self.cmd_vel.angular.z = 0
            
        elif command == 'rotate left':
            self.cmd_vel.linear.x = 0
            self.cmd_vel.angular.z = self.angular_speed
                
        elif command == 'rotate right':  
            self.cmd_vel.linear.x = 0      
            self.cmd_vel.angular.z = -self.angular_speed
            
        elif command == 'turn left':
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.angular.z += self.angular_increment
            else:        
                self.cmd_vel.angular.z = self.angular_speed
                
        elif command == 'turn right':    
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.angular.z -= self.angular_increment
            else:        
                self.cmd_vel.angular.z = -self.angular_speed
                
        elif command == 'backward':
            self.cmd_vel.linear.x = -self.speed
            self.cmd_vel.angular.z = 0
            
        elif command == 'stop': 
            # Stop the robot!  Publish a Twist message consisting of all zeros.         
            self.cmd_vel = Twist()
        
        elif command == 'faster':
            self.speed += self.linear_increment
            self.angular_speed += self.angular_increment
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.linear.x += copysign(self.linear_increment, self.cmd_vel.linear.x)
            if self.cmd_vel.angular.z != 0:
                self.cmd_vel.angular.z += copysign(self.angular_increment, self.cmd_vel.angular.z)
            
        elif command == 'slower':
            self.speed -= self.linear_increment
            self.angular_speed -= self.angular_increment
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.linear.x -= copysign(self.linear_increment, self.cmd_vel.linear.x)
            if self.cmd_vel.angular.z != 0:
                self.cmd_vel.angular.z -= copysign(self.angular_increment, self.cmd_vel.angular.z)
                
        elif command in ['quarter', 'half', 'full']:
            if command == 'quarter':
                self.speed = copysign(self.max_speed / 4, self.speed)
        
            elif command == 'half':
                self.speed = copysign(self.max_speed / 2, self.speed)
            
            elif command == 'full':
                self.speed = copysign(self.max_speed, self.speed)
            
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.linear.x = copysign(self.speed, self.cmd_vel.linear.x)

            if self.cmd_vel.angular.z != 0:
                self.cmd_vel.angular.z = copysign(self.angular_speed, self.cmd_vel.angular.z)
                
        else:
            return

        self.cmd_vel.linear.x = min(self.max_speed, max(-self.max_speed, self.cmd_vel.linear.x))
        self.cmd_vel.angular.z = min(self.max_angular_speed, max(-self.max_angular_speed, self.cmd_vel.angular.z))

    def cleanup(self):
        # When shutting down be sure to stop the robot!
        twist = Twist()
        self.cmd_vel_pub.publish(twist)
        rospy.sleep(1)

if __name__=="__main__":
    try:
        VoiceNav()
        rospy.spin()
    except rospy.ROSInterruptException:
        rospy.loginfo("Voice navigation terminated.")
可以看到,代码中定义了接收到各种命令时的控制策略。

2、仿真测试

        和前面一样,我们在rviz中进行仿真测试。
        首先是运行一个机器人模型:$ roslaunch rbx1_bringup fake_turtlebot.launch

        然后打开rviz:$ rosrun rviz rviz -d `rospack find rbx1_nav`/sim_fuerte.vcg

        如果不喜欢在终端里看输出,可以打开gui界面:$ rxconsole

        再打开语音识别的节点:$ roslaunch rbx1_speech voice_nav_commands.launch

        最后就是机器人的控制节点了:$ roslaunch rbx1_speech turtlebot_voice_nav.launch

       OK,打开上面这一堆的节点之后,就可以开始了。可以使用的命令如下:
        下图是我的测试结果,不过感觉准确度还是欠佳:

四、播放语音

        现在机器人已经可以按照我们说的话行动了,要是机器人可以和我们对话就更好了。ROS中已经集成了这样的包,下面就来尝试一下。
运行下面的命令:

  1. $ rosrun sound_play soundplay_node.py
  2. $ rosrun sound_play say.py “Greetings Humans. Take me to your leader.”

        有没有听见声音!ROS通过识别我们输入的文本,让机器人读了出来。发出这个声音的人叫做kal_diphone,如果不喜欢,我们也可以换一个人来读:

  1. $ sudo apt-get install festvox-don
  2. $ rosrun sound_play say.py “Welcome to the future” voice_don_diphone
       哈哈,这回换了一个人吧,好吧,这不也是我们的重点。
在rbx1_speech/nodes文件夹中有一个让机器人说话的节点talkback.py:
 #!/usr/bin/env python
"""
    talkback.py - Version 0.1 2012-01-10 
    Use the sound_play client to say back what is heard by the pocketsphinx recognizer.
    Created for the Pi Robot Project: http://www.pirobot.org
    Copyright (c) 2012 Patrick Goebel.  All rights reserved.

    This program is free software; you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation; either version 2 of the License, or
    (at your option) any later version.5
    
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details at:
    
    http://www.gnu.org/licenses/gpl.htmlPoint
"""
import roslib; roslib.load_manifest('rbx1_speech')
import rospy
from std_msgs.msg import String
from sound_play.libsoundplay import SoundClient
import sys

class TalkBack:
    def __init__(self, script_path):
        rospy.init_node('talkback')

        rospy.on_shutdown(self.cleanup)
        
        # Set the default TTS voice to use
        self.voice = rospy.get_param("~voice", "voice_don_diphone")
        
        # Set the wave file path if used
        self.wavepath = rospy.get_param("~wavepath", script_path + "/../sounds")
        
        # Create the sound client object
        self.soundhandle = SoundClient()
        
        # Wait a moment to let the client connect to the
        # sound_play server
        rospy.sleep(1)
        
        # Make sure any lingering sound_play processes are stopped.
        self.soundhandle.stopAll()
        
        # Announce that we are ready for input
        self.soundhandle.playWave(self.wavepath + "/R2D2a.wav")
        rospy.sleep(1)
        self.soundhandle.say("Ready", self.voice)
        
        rospy.loginfo("Say one of the navigation commands...")

        # Subscribe to the recognizer output and set the callback function
        rospy.Subscriber('/recognizer/output', String, self.talkback)
        
    def talkback(self, msg):
        # Print the recognized words on the screen
        rospy.loginfo(msg.data)
        
        # Speak the recognized words in the selected voice
        self.soundhandle.say(msg.data, self.voice)
        
        # Uncomment to play one of the built-in sounds
        #rospy.sleep(2)
        #self.soundhandle.play(5)
        
        # Uncomment to play a wave file
        #rospy.sleep(2)
        #self.soundhandle.playWave(self.wavepath + "/R2D2a.wav")

    def cleanup(self):
        self.soundhandle.stopAll()
        rospy.loginfo("Shutting down talkback node...")

if __name__=="__main__":
    try:
        TalkBack(sys.path[0])
        rospy.spin()
    except rospy.ROSInterruptException:
        rospy.loginfo("Talkback node terminated.")

         我们来运行看一下效果:$ roslaunch rbx1_speech talkback.launch

         然后再说话,ROS不仅将文本信息识别出来了,而且还读了出来,厉害吧。当然了,现在还没有加入什么人工智能的算法,不能让机器人和我们聪明的说话,不过这算是基础了,以后有时间再研究一下人工智能就更犀利了。

转载请注明来源:CV视觉网 » ROS探索总结(十)——语音控制

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