Welcome to ROSMASTER X3 repository

4. Voice controlled autonomous driving

4.1. Function description

By interacting with the voice recognition module on ROSMASTER, you can turn on or off ROSMASTER's red/blue/green/yellow line patrol function by voice. The R2 key on the handle can cancel/turn on this function at any time.

4.2. Start

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#Raspberry Pi 5 master needs to enter docker first, please perform this step
#If running the script into docker fails, please refer to ROS/07, Docker tutorial
~/run_docker.sh

4.2.1. Function package path

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~/yahboomcar_ws/src/yahboomcar_voice_ctrl/

4.2.2. Start

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roslaunch yahboomcar_voice_ctrl voice_ctrl_followline.launch #Launch chassis control + radar + wireless handle node

<PI5 needs to open another terminal to enter the same docker container

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python3 ~/yahboomcar_ws/src/yahboomcar_voice_ctrl/scripts/voice_Ctrl_follow_line.py #Enable line follow function

Taking the yellow line patrol as an example, place the ROSMASTER on the yellow line, adjust the camera position, and move the camera downwards. After the program is started, call ROSMASTER "Hello, Xiaoya" to wake up the module. When it broadcasts "in", it means waking up the module. Next, you can say "Patrol Yellow Line" to it, and ROSMASTER will broadcast "Okay, it's on." Yellow line patrol function".

Then, we release the handle's control of ROSMASTER by pressing the R2 key of the handle, and ROSMASTER begins to patrol the yellow line. If there is no remote control, you can also enter the following command through the terminal,

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rostopic pub /JoyState std_msgs/Bool False

If you want to cancel the line patrol function, say "Turn off line patrol" to ROSMASTER. ROSMASTER will stop and the voice will broadcast "OK, line patrol function has been turned off".

4.2.3. Dynamic parameter adjustment

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rosrun rqt_reconfigure rqt_reconfigure

Open the dynamic adjustment parameters, select the LineDetect column, and then adjust the parameters inside. Manually modify the adjusted parameters to voice_Ctrl_follow_line.py, and restart the program to use the adjusted parameters.

4.3. Core code analysis:

4.3.1. Import the speech recognition library and create speech recognition objects

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from Speech_Lib import Speech
self.spe = Speech()

4.3.2. Modify the value of hsv_range (process function) based on the content read through speech recognition, and then obtain the value of circle

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self.command_result = self.spe.speech_read()
self.spe.void_write(self.command_result)

if self.command_result == 23 :
self.model = "color_follow_line"
     print("red follow line")
     self.hsv_range = [(0, 106, 175), (180, 255, 255)]
            
elif self.command_result == 24 :
     self.model = "color_follow_line"
     print("green follow line")
     self.hsv_range = [(55, 105, 136), (95, 255, 255)]
    
elif self.command_result == 25 :
     self.model = "color_follow_line"
     print("bule follow line")
     self.hsv_range = [(55, 134, 218), (125, 253, 255)]
            
elif self.command_result == 26 :
     self.model = "color_follow_line"
     print("yellow follow line")
     self.hsv_range = [(17, 55, 187), (81, 255, 255)]
rgb_img, binary, self.circle = self.color.line_follow(rgb_img, self.hsv_range)

Note: The value of hsv here can be modified according to the actual situation. Since the camera is sensitive to light, the hsv value here may be different, and the line following effect is not very good. Users can adjust the maximum and minimum values of HSV with dynamic parameters, modify the max and min values of the calibrated color HSV into the above code, and use the calibrated values after restarting the program.

4.3.3. Release the speed to the chassis (execute function)

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if color_radius == 0: self.ros_ctrl.pub_cmdVel.publish(Twist())
     else:
     twist = Twist()
         b = Bool()
         [z_Pid, _] = self.PID_controller.update([(point_x - 320)/16, 0])
         if self.img_flip == True: twist.angular.z = +z_Pid
         else: twist.angular.z = -z_Pid
         twist.linear.x = self.linear
         if self.warning > 10:
             rospy.loginfo("Obstacles ahead!!!")
             self.ros_ctrl.pub_cmdVel.publish(Twist())
             self.Buzzer_state = True
             b.data = True
             self.pub_Buzzer.publish(b)
         else:
             if self.Buzzer_state == True:
                 b.data = False
                 for i in range(3): self.pub_Buzzer.publish(b)
                 self.Buzzer_state = False
             self.ros_ctrl.pub_cmdVel.publish(twist)

According to the value of self.circle obtained, it is passed into execute as an actual parameter, data is processed, whether to avoid obstacles is judged, and finally the speed topic data is released.

4.3.4, flow chart

For complete code, please refer to:

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~/yahboomcar_ws/src/yahboomcar_voice_ctrl/scripts/voice_Ctrl_follow_line.py

4.3.5. Function module communication table