In this second video we are going to see how to make Aibo execute a Motion File (MTN) using ROS.
Aibo Motion Files are files created by Sony that describe beautiful movements of Aibo. If we achieve to use those files with ROS, then we will be able to use all the already existing movement files. This will simplify, for instance, making the Aibo walk (just reproduce the walking MTN).
// VIDEO CONTENT
In this video we show: ▸ How to get the repo that contains the code that we are developing in the project ▸ How to convert MTN files to be able to use them with ROS ▸ How to send the movements of the MTN to the simulated Aibo
// RELATED LINKS
▸ The Aibo Motion File Format: http://www.dogsbodynet.com/fileformats/motion.html ▸ The repo containing the code shown on the video: https://bitbucket.org/theconstructcore/using_aibo_with_ros ▸ The ROS Development Studio: http://rds.theconstructsim.com ▸ The Academy to learn ROS online with a browser: http://rds.theconstructsim.com
Drones market is growing more and more each year, and so does the need of improving the way we control them. One of the most important topics here is, of course, how to navigate drones. In this series of videos we are going to have a look at how to implement in ROS one of the approaches that can allow us to perform Localization and Mapping in drones in a quite easy way: LSD-SLAM. LSD-SLAM is a direct monocular SLAM technique, developed by TUM, which allows to localize and create maps with drones with just a 3D camera. Hope you enjoy it!
You will learn step by step through 4 video tutorials:
[irp posts=”6638″ name=”ROS Q&A | How to Start Programming Drones using ROS”]
Part 1 – Setup the whole environment
How to perform LSD-SLAM with a ROS based Parrot AR.Drone with a Gazebo simulation. In this 1st video, you’re going to
Learn the definition of LSD SLAM (Large-Scale Direct Monocular SLAM)
Install LSD-SLAM packages and compile them
Setup the whole environment in order to have all the packages we need for performing LSD-SLAM with a Parrot AR.Drone.
Step 1. Create the project in ROS Development Studio(RDS)
You can simply build the project in RDS without any configuration in the local machine. If you haven’t had an account yet, please register here.
Step 2. Download and compile the simulation environment
Let’s clone the drone simulation under the simulation_ws, you can find shell from Tools->shell
$ cd ~/simulation_ws/src
$ git clone https://bitbucket.org/theconstructcore/tum_ardrone_sim
Now we have to compile the package before using it.
Since the package is for ROS-indigo, we have to configure something first. Please replace the following part in the file ~/simulation_ws/src/tum_ardrone_sim/tum_ardrone/src/UINode/RosThread.h
Part 2 – Solve compilation errors & Launch the nodes for performing LSD SLAM
In this 2nd video of the series, we are going to solve some compilation errors we got in the previous video, and we are trying to launch the nodes for performing LSD SLAM.
[irp posts=”8584″ name=”How to launch two drones on a Single Gazebo Simulation”]
Part 3 – Launch the LSD-SLAM ROS node in an Hector Quadrotor simulation
In this 3rd video of the series, we are successfully achieve to launch the LSD-SLAM ROS node in an Hector Quadrotor simulation.
Part 4 – Perform LSD-SLAM in an small village environment
In this 4th video of the series, we have successfully achieved to launch the LSD-SLAM ROS nodes in an Hector Quadrotor simulation, and we perform some LSD-SLAM in an small village environment.
This project is about using a Two Wheeled Mobile Robot to explore features and tools provided by ROS (Robot Operating System). We start building the robot from the scratch, using URDF (Unified Robot Description Format) and RViz to visualize it. Further, we describe the inertia and show how to simplify the URDF using XACROS. Later, motion planning algorithms, such as Obstacle Avoidance and Bugs 0, 1 and 2 are developed to be used in the built robot. Some ROS packages, like robot_localization, are used to built a map and localize on it.
In this video, we are going to explore the basics of robot modeling using the Unified Robot Description Format (URDF). At the end of this video, we will have a model ready and running in Gazebo simulator.
The directory simulation_ws is also called simulation workspace, it is supposed to contain the code and scripts relevant for simulation. For all other files we have the catkin_ws (or catkin workspace). A few more terminologies to familiarize are xacro and macro, basically xacro is a file format encoded in xml. xacro files come with extra features called macros (akin functions) that helps in reducing the amount of written text in writing robot description. Robot model description for Gazebo simulation is described in URDF model format and xacro files simplify the process of writing elaborate robot description.
Step 1.2
Now we will create a catkin package with name m2wr_description. We will add rospy as dependency. Start a SHELL from tools menu and navigate to ~simulation_ws/src directory as follows
$ cd simulation_ws/src
To create catkin package use teh following command
$ catkin_create_pkg m2wr_description rospy
At this point we should have the following directory structure
Create a directory named urdf inside m2wr_description directory. Create a file named m2wr.xacro inside the newly created urdf directory. Creating files and directories is easier (right mouse click and select appropriate option) using the IDE from the Tools menu option.
We will populate the xacro file with the following content
All of these elements have some common properties like inertial, collision and visual. The inertial and collision properties enable physics simulation and the visual property controls the appearance of the robot.
The joints help in defining the relative motion between links such as the motion of wheel with respect to the chassis. The wheels are links of cylindrical geometry, they are oriented (using rpy property) such that rolling is possible. The placement is controlled via the xyz property. For joints, we have specified the values for damping and friction as well. Now we can visualize the robot with rviz.
Step 1.3
To visualize the robot we just defined, we will create a lunch file named rviz.launch inside launch folder. We will populate it with following content:
Once the node is launched we need to open Graphical Tools from the Tools menu, it will help us to see the rviz window.
Once the rviz window loads, do the following:
Change the frame to link_chasis in Fixed Frame option
Add a Robot Description display
Step 1.4
We are now ready to simulate the robot in gazebo. We will load a empty world from the Simulations menu option
To load our robot into the empty world we will need another launch file. We will create another launch file with name spawn.launch inside the launch directory with the following contents:
Now we are ready to add control to our robot. We will add a new element called plugin to our xacro file. We will add a differential driveplugin to our robot. The new tag looks like follows:
Add this element inside the <robot> </robot> tag and relaunch the project. Now we will be able to control the robot, we can check this by listing the available topics using following command
$ rostopic list
To control the motion of the robot we can use the keyboard_teleop to publish motion commands using the keyboard. Use the following command in Shell
[irp posts=”7150″ name=”ROS Q&A | Showing my own URDF model in Gazebo”]
Exploring ROS with a 2 Wheeled Robot #Part 2 – URDF Macros
In this video, we are going to explore the macros for URDF files, using XACRO files. At the end of this video, we will have the same model organized in different files, in a organized way.
Steps continued from last part
Step 2.1
In the last 5 steps we acheived the following:
Created a xacro file that contains the urdf description of our robot
Created a launch files to spawn the robot in gazebo environment
Controlled the simulated robot using keyboard teleoperation
In this part we will organize the existing project to make it more readable and modular. Even though our robot description had only few components, we had written a lengthy xacro file. Also in robot spawn launch file we have used the following line
Here we are using the cat command to read the contents of m2wr.xacro file into robot_description parameter. However, to use the features of the xacro file we need to parse and execute the xacro file and to achieve that we will modify the above line to
The above command, uses the xacro.py file to exucute the instructions of m2wr.xacro file. A similar edit is needed for the rviz.launch file as well. So change the following line in rviz.launch file
At the moment, our xacro file does not contain any instructions. We will now split up the large m2wr.xacro file into smaller files and using the features of xacro we will assimilate the smaller files.
First we will extract the material properties from our xacro file and place them in a new file called materials.xacro. We will create a new file named materials.xacro inside the urdf folder and write the following contents into it
To test the changes we can start the rviz visualization with command
$ roslaunch m2wr_description rviz.launch
Use Graphical Tool to see the rviz output
Going further we will now remove more code from the m2wr.xacro file and place it in a new file. Create a new file with name m2wr.gazebo inside the urdf directory. We will move all the gazebo tags from the m2wr.xacro file to this new file. We will need to add the enclosing
To see whether everything works, we can launch the gazebo simulation of and empty world and spawn the robot. First we will start the gazebo simulation from the Simulations menu option and then spawn a robot with the following command
$ roslaunch m2wr_description spawn.launch
We should see the robot being spawned
Step 2.3
Next we will use macros, which are like functions, to reduce the remaining code in m2wr.xacro file.
Create a new file macro.xacro inside the urdf directory with following contents
In the above code we have defined three macros, their purpose is to take parameters and create the required element (`link` element). The first macro is named link_wheel and it accepts only one parameter name. It creates a wheel link with the name passed in the parameter. The second macro accepts three parameters name, child and origin_xyz and it creates a joint link.
We will use these macro in our robot description file (m2wr.xacro). To use macros we will replace the link element by the macros as follows
The above code is much shorter than what we started with. We have only used a few of the features available in `xacro` description.
Finally, we can test everything together by launching the gazebo simulator and spawing our robot. Start a new gazebo simulator with empty world and spawn our robot
$ roslaunch m2wr_description spawn.launch
That is it, we have optimized our code by splitting the large xacro file into other files and using macros.
The above code block will result in the following visualization
Step 3.2
The link element we just added (for acting as sensor) has random inertia property (see line 5 of the above code). We can write some sane values using a macro that will calculate the inertia values using cylinder dimensions. For this we add a new macro to our macro.xacro script. Add the following macro to the file
Now we can simulate the robot and see if everything works well. Load an empty world in gazebo simulator window. Also open a Shell window and run the following command
$ roslaunch m2wr_description spawn.launch
Next we need to add the sensor beharior to the link. To do so we will use the laser gazebo plugin. Information about this plugin is available here. Open the m2wr.gazebo file and add the following plugin element
This code specifies many important parameters Update rate : Controls how often (how fast) the laser data is captured samples : Defines how how many readings are contained in one scan resolution : Defines the minimum angular distance between readings captured in a laser scan range : Defines the minimum sense distance and maximum sense distance. If a point is below minimum sense its reading becomes zero(0) and if a point is further than the maximum sense distance its reading becomes inf. The range resolution defines the minimum distance between 2 points such that two points can be resolved as two separate points. noise : This parameter lets us add gaussian noise to the range data captured by the sensor topicName : Defines the name which is used for publishing the laser data frameName : Defines the link to which the plugin has to be applied
With this plugin incorporated in the urdf file we are now ready to simulate and visualize the laser scan in action. Start the empty world and spawn the robot by launching the spawn.launch file in a Shell. To verify the working of the scan sensor check the list of topics in Shell window with following command
$ rostopic list
You should get /m2wr/laser/scan in the list of topics
Step 3.3
We will visualize the laser scan data with rviz. First we will populate the robot environment with a few obstacles to better see the laser scan result. Use the box icon on top right of gazebo window to create a few box type obstacles (simply click and drop)
To start rviz visualization launch the rviz.launch file in a new Shell and use Graphical Tool window to load the visualization. Use the following command to launch rviz
$ roslaunch m2wr_description rviz.launch
After starting rviz open the Graphical Tools window. Once rviz window loads you need to do the following settings
Select odom in the Fixed Frame field (see the image below)
Add two new displays using the Add button on the left bottom of rviz screen. The first display should be RobotModel and the other should be LaserScan
Expand the LaserScan display by double clicking on its name and choose Topic as /m2wr/laser/scan (as shown in image below)
Now when we move the robot we can see the laser scan changing.
In this video, we are going to read the values of the laser scanner and filter a small part to work with
Steps to recreate the project as shown in the video
Step 4.1
Head to ROS Development Studio and create a new project. Provide a suitable project name and some useful description. (We have named the project video_no_4)
Open the project (this will take few seconds).
We will clone the github repository to start. Open a Shell from the Tools menu and run the following commands in the Shell
The package m2wr_description contains the project files developed so far (i.e. robot + laser scan sensor). The other package my_worlds contains two directories
launch : Contains a launch file (we will use it shortly)
worlds : Contains multiple world description files
From Simulations menu, choose the option Select launch file… and select the world1.launch option
Step 4.2
We will create a new catkin package named motion_plan with dependencies rospy, std_msgs, geometry_msgs and sensor_msgs. Open a Shell from the Tools menu and write the following commands
$ cd ~/catkin_ws/src $ catkin_create_pgk motion_plan rospy std_msgs geometry_msgs sensor_msgs $ cd motion_plan $ mkdir scripts $ touch scripts/reading_laser.py
These commands will create a directory (named scripts) inside the motion_plan package. This directory will contains a python scripts (reading_laser.py) that we will use to read the laser scan data coming on the /m2wr/laser/scan topic (we created this topic in last part). Add the following code to the reading_laser.py file
The above code converts the 720 readings contained inside the LaserScan msg into five distinct readings. Each reading is the minimum distance measured on a sector of 60 degrees (total 5 sectors = 180 degrees).
Lets run this code again and see the data
Step 4.3
We can see the changes in range measurements as we move the robot near to the boundaries. One noticable thing is the inf value that is measured when the wall is away.
We can modify the code to change this value to read maximum range. The changed code is
Start a simulation using the Simulations menu option. Select the world.launch option and launch the simulation.
Now we will take a look at the obstacle avoidance algorithm. Open the IDE and browse to the file ~/catkin_ws/src/two-wheeled-robot-motion-planning/scripts/obstacle_avoidance.py. There are 3 functions defined in this file:
main This is the entry point of the file. This function sets up a Subscriber to the laser scan topic /m2wr/laser/scan and a Publisher to /cmd_vel topic.
def main():
global pub
rospy.init_node('reading_laser')
pub = rospy.Publisher('/cmd_vel', Twist, queue_size=1)
sub = rospy.Subscriber('/m2wr/laser/scan', LaserScan, clbk_laser)
rospy.spin()
clbk_laser We need to provide a callback function to the Subscriber defined in main, for this purpose we have this function. It receives laser scan data comprising of 720 readings and converts it into 5 readings (details in part 4 video).
take_action This function implements the obstacle avoidance logic. Based on the distances sensed in the five region (left, center-left, center, center-right, right). We consider possible combinations for obstacles, once we identify the obstacle configuration we steer the robot away from obstacle.
def take_action(regions):
msg = Twist()
linear_x = 0
angular_z = 0
state_description = ''
if regions['front'] > 1 and regions['fleft'] > 1 and regions['fright'] > 1:
state_description = 'case 1 - nothing'
linear_x = 0.6
angular_z = 0
elif regions['front'] < 1 and regions['fleft'] > 1 and regions['fright'] > 1:
state_description = 'case 2 - front'
linear_x = 0
angular_z = 0.3
elif regions['front'] > 1 and regions['fleft'] > 1 and regions['fright'] < 1:
state_description = 'case 3 - fright'
linear_x = 0
angular_z = 0.3
elif regions['front'] > 1 and regions['fleft'] < 1 and regions['fright'] > 1:
state_description = 'case 4 - fleft'
linear_x = 0
angular_z = -0.3
elif regions['front'] < 1 and regions['fleft'] > 1 and regions['fright'] < 1:
state_description = 'case 5 - front and fright'
linear_x = 0
angular_z = 0.3
elif regions['front'] < 1 and regions['fleft'] < 1 and regions['fright'] > 1:
state_description = 'case 6 - front and fleft'
linear_x = 0
angular_z = -0.3
elif regions['front'] < 1 and regions['fleft'] < 1 and regions['fright'] < 1:
state_description = 'case 7 - front and fleft and fright'
linear_x = 0
angular_z = 0.3
elif regions['front'] > 1 and regions['fleft'] < 1 and regions['fright'] < 1:
state_description = 'case 8 - fleft and fright'
linear_x = 0.3
angular_z = 0
else:
state_description = 'unknown case'
rospy.loginfo(regions)
rospy.loginfo(state_description)
msg.linear.x = -linear_x
msg.angular.z = angular_z
pub.publish(msg)
To test the logic lets run the simulation. We have the world loaded, now we will spawn the differential drive robot with following command
$ roslaunch m2wr_description spawn.launch
Finally we launch the obstacle avoidance script to move the robot around and avoid obstacles
$ rosrun motion_plan obstacle_avoidance.py
That finishes the instructions. You can change various settings like speed of robot, sensing distance etc and see how it works.
In this video, we are going create an algorithm to go from a point to another using the odometry data to localize the robot.
Steps to recreate the project as shown in the video (continued from part 5)
Step 6.1
In this part we are going to implement a simple navigation algorithm to move our robot from any point to a desired point. We will use the concept of state machines to implement the navigation logic. In a state machine there are finite number of states that represent the current situation (or behavior) of the system. In our case, we have three states
Fix Heading : Denotes the state when robot heading differs from the desired heading by more than a threshold (represented by yaw_precision_ in code)
Go Straight : Denotes the state when robot has correct heading but is away from the desired point by a distance greater than some threshold ( represented by dist_precision_ in code)
Done : Denotes the state when robot has correct heading and has reached the destination.
The robot can be in any one state at a time and can switch to other states as different conditions arise. This is depicted by the following state transition diagram
To implement this state logic lets create a new python script inside the ~/catkin_ws/src/two-wheeled-robot-motion-planning/scripts/ directory named go_to_point.py with following content
Lets analyze the contents of this script. We have defined the following function
main This is the entry point of the file. This function sets up a Subscriber to the odometry topic /odom and a Publisher to /cmd_vel topic to command velocity of the robot. Further this function processes the state machine depending on the value of state variable.
clbk_odom This is a callback function for the Subscriber defined in main. This function receives the odometry data and extracts the position and yaw information. The odometry data encodes orientation information in quaternions. To obtains yaw the quaternion is converted into euler angles (line 43)
change_state This function changes the value of the global state variable that stores the robot state information.
fix_yaw This function is executed when robot is in state 0 (Fix heading). First the current heading of the robot is checked with desired heading. If the differene in heading is more than a threshold the robot is commanded to turn in its place.
go_straight_ahead This function is executed when robot is in state 1 (Go Straight). This state occurs after robot has fixed the error in yaw. In this state, the distance between the robots current position and desired position is compared with a threshold. If robot is further away from desired position it is commanded to move forward. If the current position lies closer to the desired position then the yaw is again checked for error, if yaw is significantly different from the desired yaw value the robot goes to state 0.
done Eventually robot achieves correct heading and correct position. Once in this state the robot stops.
Step 6.2
Start a simulation using the Simulations menu option. Select the world.launch option and launch the simulation.
To test the logic lets run the simulation. We have the world loaded, now we will spawn the differential drive robot with following command
$ roslaunch m2wr_description spawn.launch
Finally we launch the obstacle avoidance script to move the robot around and avoid obstacles
$ rosrun motion_plan obstacle_avoidance.py
Now you should see the robot navigate to the point given in the go_to_point.py script ( line 19-21) from its current location. With the navigation implemented we have finished the part 6 of the series.
Wall Following Robot Algorithm – Two Wheeled Robot #Part 7
In this video, we are going work with wall following robot algorithm. We’ll start watching the demo, then let’s go straight to the code and understand line by line how to perform the task.
Steps to recreate the project as shown in the video
Step 7.1
In this part we will write an algorithm to make the robot follow a wall. We can continue from the last part or start with a new project. These instructions are for starting with a new project.
Launch the simulation with Simulations > Select launch file > my_worlds > world.launch
Spawn the robot with following command
$ roslaunch m2wr_description spawn.launch
The wall following algorithm is written inside the follow_wall.py script located within the ~catkin_ws/src/two-wheeled-robot-motion-planning/scripts/ directory. Open Tools>IDE and browse to this script. It contains following code
#! /usr/bin/env python
import rospy
from sensor_msgs.msg import LaserScan
from geometry_msgs.msg import Twist
from nav_msgs.msg import Odometry
from tf import transformations
import math
pub_ = None
regions_ = {
'right': 0,
'fright': 0,
'front': 0,
'fleft': 0,
'left': 0,
}
state_ = 0
state_dict_ = {
0: 'find the wall',
1: 'turn left',
2: 'follow the wall',
}
def clbk_laser(msg):
global regions_
regions_ = {
'right': min(min(msg.ranges[0:143]), 10),
'fright': min(min(msg.ranges[144:287]), 10),
'front': min(min(msg.ranges[288:431]), 10),
'fleft': min(min(msg.ranges[432:575]), 10),
'left': min(min(msg.ranges[576:713]), 10),
}
take_action()
def change_state(state):
global state_, state_dict_
if state is not state_:
print 'Wall follower - [%s] - %s' % (state, state_dict_[state])
state_ = state
def take_action():
global regions_
regions = regions_
msg = Twist()
linear_x = 0
angular_z = 0
state_description = ''
d = 1.5
if regions['front'] > d and regions['fleft'] > d and regions['fright'] > d:
state_description = 'case 1 - nothing'
change_state(0)
elif regions['front'] < d and regions['fleft'] > d and regions['fright'] > d:
state_description = 'case 2 - front'
change_state(1)
elif regions['front'] > d and regions['fleft'] > d and regions['fright'] < d:
state_description = 'case 3 - fright'
change_state(2)
elif regions['front'] > d and regions['fleft'] < d and regions['fright'] > d:
state_description = 'case 4 - fleft'
change_state(0)
elif regions['front'] < d and regions['fleft'] > d and regions['fright'] < d:
state_description = 'case 5 - front and fright'
change_state(1)
elif regions['front'] < d and regions['fleft'] < d and regions['fright'] > d:
state_description = 'case 6 - front and fleft'
change_state(1)
elif regions['front'] < d and regions['fleft'] < d and regions['fright'] < d:
state_description = 'case 7 - front and fleft and fright'
change_state(1)
elif regions['front'] > d and regions['fleft'] < d and regions['fright'] < d:
state_description = 'case 8 - fleft and fright'
change_state(0)
else:
state_description = 'unknown case'
rospy.loginfo(regions)
def find_wall():
msg = Twist()
msg.linear.x = 0.2
msg.angular.z = -0.3
return msg
def turn_left():
msg = Twist()
msg.angular.z = 0.3
return msg
def follow_the_wall():
global regions_
msg = Twist()
msg.linear.x = 0.5
return msg
def main():
global pub_
rospy.init_node('reading_laser')
pub_ = rospy.Publisher('/cmd_vel', Twist, queue_size=1)
sub = rospy.Subscriber('/m2wr/laser/scan', LaserScan, clbk_laser)
rate = rospy.Rate(20)
while not rospy.is_shutdown():
msg = Twist()
if state_ == 0:
msg = find_wall()
elif state_ == 1:
msg = turn_left()
elif state_ == 2:
msg = follow_the_wall()
pass
else:
rospy.logerr('Unknown state!')
pub_.publish(msg)
rate.sleep()
if __name__ == '__main__':
main()
Lets analyze the contents of this scripts:
There are a few globalvariables
pub_ : this variable is a reference to the publisher that will be initialized inside the main function
regions_ : this is a dictionary variable that holds the distances to five directions
state_ : this variable stores the current state of the robot
state_dict_ : this variable holds the possible states
The functions defined are:
main : This is the entry point for the algorithm, it initializes a node, a publisher and a subscriber. Depending on the value of the state_ variable, a suitable control action is taken (by calling other functions). This function also configures the frequency of execution of control action using Rate function.
clbk_laser : This function is passed to the Subscriber method and it executes when a new laser data is made available. This function writes distance values in the global variable regions_ and calls the function take_actions
take_action : This function manipulates the state of the robot. The various distances stored in regions_ variable help in determining the state of the robot.
find_wall : This function defines the action to be taken by the robot when it is not surrounded by any obstacle. This method essentially makes the robot move in a anti-clockwise circle (until it finds a wall).
turn_left : When the robot detects an obstacle it executes the turn left action
follow_the_wall : Once the robot is positioned such that its front and front-left path is clear while its front-right is obstructed the robot goes into the follow wall state. In this state this function is executed and this function makes the robot to follow a straight line.
This is the overall logic that governs the wall following behavior of the robot.
Step 7.3
Let us run the wall following algorithm and see the robot in action. We already have the simulation window open with robot spawned into it. Open Tools>Shell and enter the following command
In this video, the 8th of the series Exploring ROS with a 2 Wheeled Robot, we are gonna work with the Bug 0 algorithm using the previous scripts we have created before: Wall following + Go to point
Steps to recreate the project as shown in the video
Step 8.1
In this part we are going to program Bug 0 behavior for our mobile robot. Basic requirements of Bug 0 algorithm are:
direction to goal should be known
Wall sensing ability
We will start by creating a new project and cloning the project files in our project.
The script bug0.py inside the catkin_ws/src/two-wheeled-robot-motion-planning/scripts/ implements the Bug 0 beharior. In short, the Bug 0 algorithm drives the robot towards a points (goal), while doing so if the robot detects an obstacle it goes around it.
The important functions we have defines in the bug0.py script are:
main : The entry point of the program. It initializes a node, two subscribers (laser scan and odometry) and two Service clients (go_to_point_switch and wall_follower_switch). A state based logic is used to drive robot towards the goal position. Initially the robot is put in Go to point state, and when an obstacle is detected the state is switched to Wall following state. When there is a straight free path towards the goal the robot again switches to state Go to point.
change_state : This function accepts a state argument and calls the respective service handler.
Other functions are similar to those implemented before in part 7 (eg. clbk_odom, clbk_laser and normalize_angle).
Also we have done changes to previously created scripts follow_wall.py and go_to_point.py. These scripts now implement an additional service server. The server helps in activating and deactivating the execution of respective algorithm based on the state maintained in the bug0.py script.
For example, when we are in Go to point state we communicate the server (go_to_point_switch) to activate go_to_point algorithm and we deactivate the other server (wall_follower_switch).
Here is the code for bug0.py script for reference:
#! /usr/bin/env python
import rospy
# import ros message
from geometry_msgs.msg import Point
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from tf import transformations
# import ros service
from std_srvs.srv import *
import math
srv_client_go_to_point_ = None
srv_client_wall_follower_ = None
yaw_ = 0
yaw_error_allowed_ = 5 * (math.pi / 180) # 5 degrees
position_ = Point()
desired_position_ = Point()
desired_position_.x = rospy.get_param('des_pos_x')
desired_position_.y = rospy.get_param('des_pos_y')
desired_position_.z = 0
regions_ = None
state_desc_ = ['Go to point', 'wall following']
state_ = 0
# 0 - go to point
# 1 - wall following
# callbacks
def clbk_odom(msg):
global position_, yaw_
# position
position_ = msg.pose.pose.position
# yaw
quaternion = (
msg.pose.pose.orientation.x,
msg.pose.pose.orientation.y,
msg.pose.pose.orientation.z,
msg.pose.pose.orientation.w)
euler = transformations.euler_from_quaternion(quaternion)
yaw_ = euler[2]
def clbk_laser(msg):
global regions_
regions_ = {
'right': min(min(msg.ranges[0:143]), 10),
'fright': min(min(msg.ranges[144:287]), 10),
'front': min(min(msg.ranges[288:431]), 10),
'fleft': min(min(msg.ranges[432:575]), 10),
'left': min(min(msg.ranges[576:719]), 10),
}
def change_state(state):
global state_, state_desc_
global srv_client_wall_follower_, srv_client_go_to_point_
state_ = state
log = "state changed: %s" % state_desc_[state]
rospy.loginfo(log)
if state_ == 0:
resp = srv_client_go_to_point_(True)
resp = srv_client_wall_follower_(False)
if state_ == 1:
resp = srv_client_go_to_point_(False)
resp = srv_client_wall_follower_(True)
def normalize_angle(angle):
if(math.fabs(angle) > math.pi):
angle = angle - (2 * math.pi * angle) / (math.fabs(angle))
return angle
def main():
global regions_, position_, desired_position_, state_, yaw_, yaw_error_allowed_
global srv_client_go_to_point_, srv_client_wall_follower_
rospy.init_node('bug0')
sub_laser = rospy.Subscriber('/m2wr/laser/scan', LaserScan, clbk_laser)
sub_odom = rospy.Subscriber('/odom', Odometry, clbk_odom)
srv_client_go_to_point_ = rospy.ServiceProxy('/go_to_point_switch', SetBool)
srv_client_wall_follower_ = rospy.ServiceProxy('/wall_follower_switch', SetBool)
# initialize going to the point
change_state(0)
rate = rospy.Rate(20)
while not rospy.is_shutdown():
if regions_ == None:
continue
if state_ == 0:
if regions_['front'] > 0.15 and regions_['front'] < 1:
change_state(1)
elif state_ == 1:
desired_yaw = math.atan2(desired_position_.y - position_.y, desired_position_.x - position_.x)
err_yaw = normalize_angle(desired_yaw - yaw_)
if math.fabs(err_yaw) < (math.pi / 6) and \
regions_['front'] > 1.5:
change_state(0)
if err_yaw > 0 and \
math.fabs(err_yaw) > (math.pi / 4) and \
math.fabs(err_yaw) < (math.pi / 2) and \
regions_['left'] > 1.5:
change_state(0)
if err_yaw < 0 and \
math.fabs(err_yaw) > (math.pi / 4) and \
math.fabs(err_yaw) < (math.pi / 2) and \
regions_['right'] > 1.5:
change_state(0)
rate.sleep()
if __name__ == "__main__":
main()
Lastly, we have defined a new launch file (inside directory ~catkin_ws/src/two-wheeled-robot-motion-planning/launch) that will help us run all these scripts. We can manually run individual scripts too but that is tiresome. The script behaviors.launch helps us launch the two behaviors i.e. wall follower and go to point. Here are the contents this launch file for reference
In this video, the 9th of the series Exploring ROS with a 2 Wheeled Robot, we are gonna see the Bug 0 Foil, why it happens and how it is improved using the Bug 1 behavior.
Below are the steps to replicate the project as shown in the video
Step 9.1
We will start by creating a new project and cloning the project files in our project.
Notice that the robot moves in the circumference of the new fencing structure in the world, never reaching the goal position (as shown)
Thus we see the inherent drawback of the Bug 0 algorithm. We can do better using the Bug 1 algorithm as depicted in the image below
The Bug 1 algorithm moves the robot about the obstacle (circumnavigate). When the robot passes near the goal it records this point and keeps on circumnavigating the obstacle. Once the robot reaches the initial point (where the robot first met the obstacle) it then goes to the point stored in memory and then moves towards the goal from there.
We can see that Bug 1 algorithm, though lengthy, works better in situations where Bug 0 will fail.
In the next part we will implement the Bug 1 algorithm. This finishes the part 9.
While the robot performs the navigation, we can analyze the structure and contents of Bug 1 algorithm
Here is the code for the bug1.py script contained in ~/catkin_ws/src/two-wheeled-robot-motion-planning/scripts/ directory:
import rospy
# import ros message
from geometry_msgs.msg import Point
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from tf import transformations
# import ros service
from std_srvs.srv import *
import math
srv_client_go_to_point_ = None
srv_client_wall_follower_ = None
yaw_ = 0
yaw_error_allowed_ = 5 * (math.pi / 180) # 5 degrees
position_ = Point()
desired_position_ = Point()
desired_position_.x = rospy.get_param('des_pos_x')
desired_position_.y = rospy.get_param('des_pos_y')
desired_position_.z = 0
regions_ = None
state_desc_ = ['Go to point', 'circumnavigate obstacle', 'go to closest point']
state_ = 0
circumnavigate_starting_point_ = Point()
circumnavigate_closest_point_ = Point()
count_state_time_ = 0 # seconds the robot is in a state
count_loop_ = 0
# 0 - go to point
# 1 - circumnavigate
# 2 - go to closest point
# callbacks
def clbk_odom(msg):
global position_, yaw_
# position
position_ = msg.pose.pose.position
# yaw
quaternion = (
msg.pose.pose.orientation.x,
msg.pose.pose.orientation.y,
msg.pose.pose.orientation.z,
msg.pose.pose.orientation.w)
euler = transformations.euler_from_quaternion(quaternion)
yaw_ = euler[2]
def clbk_laser(msg):
global regions_
regions_ = {
'right': min(min(msg.ranges[0:143]), 10),
'fright': min(min(msg.ranges[144:287]), 10),
'front': min(min(msg.ranges[288:431]), 10),
'fleft': min(min(msg.ranges[432:575]), 10),
'left': min(min(msg.ranges[576:719]), 10),
}
def change_state(state):
global state_, state_desc_
global srv_client_wall_follower_, srv_client_go_to_point_
global count_state_time_
count_state_time_ = 0
state_ = state
log = "state changed: %s" % state_desc_[state]
rospy.loginfo(log)
if state_ == 0:
resp = srv_client_go_to_point_(True)
resp = srv_client_wall_follower_(False)
if state_ == 1:
resp = srv_client_go_to_point_(False)
resp = srv_client_wall_follower_(True)
if state_ == 2:
resp = srv_client_go_to_point_(False)
resp = srv_client_wall_follower_(True)
def calc_dist_points(point1, point2):
dist = math.sqrt((point1.y - point2.y)**2 + (point1.x - point2.x)**2)
return dist
def normalize_angle(angle):
if(math.fabs(angle) > math.pi):
angle = angle - (2 * math.pi * angle) / (math.fabs(angle))
return angle
def main():
global regions_, position_, desired_position_, state_, yaw_, yaw_error_allowed_
global srv_client_go_to_point_, srv_client_wall_follower_
global circumnavigate_closest_point_, circumnavigate_starting_point_
global count_loop_, count_state_time_
rospy.init_node('bug1')
sub_laser = rospy.Subscriber('/m2wr/laser/scan', LaserScan, clbk_laser)
sub_odom = rospy.Subscriber('/odom', Odometry, clbk_odom)
rospy.wait_for_service('/go_to_point_switch')
rospy.wait_for_service('/wall_follower_switch')
srv_client_go_to_point_ = rospy.ServiceProxy('/go_to_point_switch', SetBool)
srv_client_wall_follower_ = rospy.ServiceProxy('/wall_follower_switch', SetBool)
# initialize going to the point
change_state(0)
rate_hz = 20
rate = rospy.Rate(rate_hz)
while not rospy.is_shutdown():
if regions_ == None:
continue
if state_ == 0:
if regions_['front'] > 0.15 and regions_['front'] < 1:
circumnavigate_closest_point_ = position_
circumnavigate_starting_point_ = position_
change_state(1)
elif state_ == 1:
# if current position is closer to the goal than the previous closest_position, assign current position to closest_point
if calc_dist_points(position_, desired_position_) < calc_dist_points(circumnavigate_closest_point_, desired_position_):
circumnavigate_closest_point_ = position_
# compare only after 5 seconds - need some time to get out of starting_point
# if robot reaches (is close to) starting point
if count_state_time_ > 5 and \
calc_dist_points(position_, circumnavigate_starting_point_) < 0.2:
change_state(2)
elif state_ == 2:
# if robot reaches (is close to) closest point
if calc_dist_points(position_, circumnavigate_closest_point_) < 0.2:
change_state(0)
count_loop_ = count_loop_ + 1
if count_loop_ == 20:
count_state_time_ = count_state_time_ + 1
count_loop_ = 0
rate.sleep()
if __name__ == "__main__":
main()
Following are the important changes:
Addition of new states in the robot states. These states are circumnavigate obstacle and go to closest point, as discussed in the previous part, the first one makes the robot go around the obstacle perimeter and the second state makes the robot to navigate to the point (on obstacle perimeter) that takes the robot closest to the goal.
We have new variables to store the start point and the closest point.
The change state function is similar to that in Bug 0 with more number of states (3) :
State 1 : Go to point : Uses the go_to_point algorithm
State 2 : circumnavigate obstacle : Uses the follow_wall algorithm
State 3 : go to closest point : Uses the follow_wall algorithm
In the last two states, we are using the same algorithm but there is a difference of the stop point. For the state 2, the stop point is the start pont (circumnavigate) while for the state 3 the stop point is the closest point
A new function calc_dist_points is defined for calculating the distance of goal point from the current location of the robot.
Lastly, there is an additional variable that stores the number of seconds elapsed while in a state. This variable helps us in determining if we have completed the circumnavigation otherwise there can be ambiguity when the robot has to change from state 2 to state 3.
From ROS Indigo to Kinetic – Exploring ROS with a 2 wheeled Robot – Part 11
In this video, the 11th of the series, you’ll see the necessary changes in the project to make it work with ROS Kinetic, the new version supported by RDS (ROS Development Studio).
Steps to recreate the project as shown in the video
Step 11.1
Lets start by creating a new project on RDS and cloning the project files from online repository.
At the time of creation of these video tutorials, RDS got upgraded from ROS Indigo to ROS Kinetic. While the code we have written in previous parts works fine, we can improve upon the organization and improve usability of the code. Thus, we have made the following changes to the project since the last part.
RemovedLegacy mode from the differential drive plugin (otherwise we see warning)
Some minor modification to the launch file. Earlier we had the following syntax to import a file<param name="robot_description" command="$(find xacro)xacro.py '$(find mono_bot)/urdf/mono_b.xacro'" /> Now we need not write the .py extension and also need to add –inorder argument <param name="robot_description" command="$(find xacro)xacro --inorder '$(find mono_bot)/urdf/mono_b.xacro'" />
Next the macro tags are also fixed in macro.xacro file<xacro name="link_wheel" params="name"> changes to <xacro:macro name="link_wheel" params="name">
Lastly the opening element (robot tag) needs a modification too<robot> will change to <robot xmlns:xacro="https://www.ros.org/wiki/xacro">
Next, we will integrate the robot spawning code into the simulation launch file. This will make the job of starting a simulation easier and faster as now the robot will automatically get spawned in a desired location when simulation starts.
Here is the launch file code (bug1.launch) for reference
Finally, we will create a script to do a robot position reset because everytime we make some change to our algorithm we need to start the simulation again and again. With the help of script we will not need to restart the simulation. This is possible because gazebo provides a node called /gazebo/set_model_state to set the model state. The following script shows how this is done
We can incorporate this same code (which is actually done inside bug1.py) to move the robot at a desired location before starting our navigation algorithm.
Finally we can test the changes made by launching the project. Start the simulation from Simulations > Select launch file… > my_worlds > bug1.launch. Then execute the bug1 algorithm by opening Tools > Shell and enter commands
$ roslaunch motion_plan bug1.launch
Now you will see the robot navigating towards the goal. To modify the robot initial position and goal position, we have defined arguments in this launch file (bug1.launch). That finishes this part.
[irp posts=”8349″ name=”How to add a rotating join to Kinect in Turtlebot”]
Bug 2 – Exploring ROS with a 2 wheeled robot #Part 12
Motion planning algorithms – In this video we show the implemented code for Bug 2 behavior and a simulation using it as well. From planning the line between starting and desired points, going straight to the point and following obstacles. Using the same robot and code we have been developing in this series.
Steps to recreate the project as shown in the video
Step 12.1
Lets start by creating a new project on RDS and cloning the project files from online repository.
Run a simulation. Open Simulations > Select launch file… > my_worlds > world2.launch.
Open the script bug2.py located inside ~catkin_ws/src/two-wheeled-robot-motion-planning/scripts directory
This script (bug2.py) contains the code for the new Bug 2 navigation algorithm. Lets us analyze the contents of this script. Here are the contents of this file for reference
import rospy
# import ros message
from geometry_msgs.msg import Point
from sensor_msgs.msg import LaserScan
from nav_msgs.msg import Odometry
from tf import transformations
from gazebo_msgs.msg import ModelState
from gazebo_msgs.srv import SetModelState
# import ros service
from std_srvs.srv import *
import math
srv_client_go_to_point_ = None
srv_client_wall_follower_ = None
yaw_ = 0
yaw_error_allowed_ = 5 * (math.pi / 180) # 5 degrees
position_ = Point()
initial_position_ = Point()
initial_position_.x = rospy.get_param('initial_x')
initial_position_.y = rospy.get_param('initial_y')
initial_position_.z = 0
desired_position_ = Point()
desired_position_.x = rospy.get_param('des_pos_x')
desired_position_.y = rospy.get_param('des_pos_y')
desired_position_.z = 0
regions_ = None
state_desc_ = ['Go to point', 'wall following']
state_ = 0
count_state_time_ = 0 # seconds the robot is in a state
count_loop_ = 0
# 0 - go to point
# 1 - wall following
# callbacks
def clbk_odom(msg):
global position_, yaw_
# position
position_ = msg.pose.pose.position
# yaw
quaternion = (
msg.pose.pose.orientation.x,
msg.pose.pose.orientation.y,
msg.pose.pose.orientation.z,
msg.pose.pose.orientation.w)
euler = transformations.euler_from_quaternion(quaternion)
yaw_ = euler[2]
def clbk_laser(msg):
global regions_
regions_ = {
'right': min(min(msg.ranges[0:143]), 10),
'fright': min(min(msg.ranges[144:287]), 10),
'front': min(min(msg.ranges[288:431]), 10),
'fleft': min(min(msg.ranges[432:575]), 10),
'left': min(min(msg.ranges[576:719]), 10),
}
def change_state(state):
global state_, state_desc_
global srv_client_wall_follower_, srv_client_go_to_point_
global count_state_time_
count_state_time_ = 0
state_ = state
log = "state changed: %s" % state_desc_[state]
rospy.loginfo(log)
if state_ == 0:
resp = srv_client_go_to_point_(True)
resp = srv_client_wall_follower_(False)
if state_ == 1:
resp = srv_client_go_to_point_(False)
resp = srv_client_wall_follower_(True)
def distance_to_line(p0):
# p0 is the current position
# p1 and p2 points define the line
global initial_position_, desired_position_
p1 = initial_position_
p2 = desired_position_
# here goes the equation
up_eq = math.fabs((p2.y - p1.y) * p0.x - (p2.x - p1.x) * p0.y + (p2.x * p1.y) - (p2.y * p1.x))
lo_eq = math.sqrt(pow(p2.y - p1.y, 2) + pow(p2.x - p1.x, 2))
distance = up_eq / lo_eq
return distance
def normalize_angle(angle):
if(math.fabs(angle) > math.pi):
angle = angle - (2 * math.pi * angle) / (math.fabs(angle))
return angle
def main():
global regions_, position_, desired_position_, state_, yaw_, yaw_error_allowed_
global srv_client_go_to_point_, srv_client_wall_follower_
global count_state_time_, count_loop_
rospy.init_node('bug0')
sub_laser = rospy.Subscriber('/m2wr/laser/scan', LaserScan, clbk_laser)
sub_odom = rospy.Subscriber('/odom', Odometry, clbk_odom)
rospy.wait_for_service('/go_to_point_switch')
rospy.wait_for_service('/wall_follower_switch')
rospy.wait_for_service('/gazebo/set_model_state')
srv_client_go_to_point_ = rospy.ServiceProxy('/go_to_point_switch', SetBool)
srv_client_wall_follower_ = rospy.ServiceProxy('/wall_follower_switch', SetBool)
srv_client_set_model_state = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState)
# set robot position
model_state = ModelState()
model_state.model_name = 'm2wr'
model_state.pose.position.x = initial_position_.x
model_state.pose.position.y = initial_position_.y
resp = srv_client_set_model_state(model_state)
# initialize going to the point
change_state(0)
rate = rospy.Rate(20)
while not rospy.is_shutdown():
if regions_ == None:
continue
distance_position_to_line = distance_to_line(position_)
if state_ == 0:
if regions_['front'] > 0.15 and regions_['front'] < 1:
change_state(1)
elif state_ == 1:
if count_state_time_ > 5 and \
distance_position_to_line < 0.1:
change_state(0)
count_loop_ = count_loop_ + 1
if count_loop_ == 20:
count_state_time_ = count_state_time_ + 1
count_loop_ = 0
rospy.loginfo("distance to line: [%.2f], position: [%.2f, %.2f]", distance_to_line(position_), position_.x, position_.y)
rate.sleep()
if __name__ == "__main__":
main()
The number of states is again 2 (as in Bug 0) Go to point and wall following.
We have one more function distance_to_line(). It calculates the distance of the robot from the imaginary line that joins initial position of the robot with the desired position of the robot.
The idea of Bug 2 algorithm is to follow this imaginary line in absense of obstacle (remember go to point). When an obstacle shows up, the robot starts circumnavigating it till it again finds itself close to the imaginary line.
Rest of the code is similar to those of the previous parts (callbacks and state transition logic)
Also note that we have made use of the count_state_time_ variable to track time elapsed since changing state. This helps us to wrongly triggering state transition on the first contact with the imaginary line on first encounter. We let some time go by before we seek to find the imaginary line after we have started circumnavigating the obstacle.
Now we can go ahead and run the code. Open Tools > Shell and enter commands
$ roslaunch motion_plan bug2.launch
The robot in the simulation will start the robot motion. With that we have finished the part 12.
GMapping – Exploring ROS with a 2 wheeled robot #Part 13
In this video we are going to use ROS GMapping in our 2 wheeled robot, the one used in the previous videos, to generate a map using SLAM technique. We are using the robot Laser Scan and Odometry data to generate the map.
Steps to recreate the project as shown in the video
Step 13.1
Lets start by creating a new project on RDS and cloning the project files from online repository.
In this part we are going to work with the gmapping package. The Gmapping package package helps in creating the map of the robot environment and it requires the following informations to create environment map
Laser scan
Odometry information
Sensor to robot base transform (named link_chassis in our robot urdf model)
The project we cloned contains the launch file to start the project. Open Tools > IDE, browse to ~catkin_ws/src/two-wheeled-robot-motion-planning/launch/ directory and load the gmapping.launch file. Here are the contents of this file for reference
The various elements and their utility is discussed next
Topics:
scan_topic : points to the laser scan topic (/m2wr/laser/scan)
base_frame : points to the robot base element (link_chassis)
odom_frame : point to the odometry topic (/odom)
Transform:
robot_state_publisher : publishes the transform of laser scanner with respect to robots chassis (link_chassis)
Rviz : To visualize the map data and robot.
Gmapping node: This node is responsible for doing the mapping. We have specified various important arguments to use this package. Most of these values come from the example given in the wiki.
Lets launch the simulation and see the robot in action. Load the Bug 1 scene , open Simulations > Select launch file… > my_worlds > bug1.launch. We need to start a Graphical Tool from Tools > Graphical Tools menu to see the map in rviz. Also we will need a Shell to launch the Gmapping package.
Once the rviz window appears in the Graphical Tools, use the Add button (left bottom) to add a laser_scan and map display to the pane.
Now we can start another Shell and start the Bug 1 algorithm to make the robot move. While the robot moves and registers the laser scan data we will see the map building.
As the robot moves around in the world we see the map coming up. With this we have finishes the series. To learn more concepts ROS use the following resources.
![[ROS Projects] – Making Aibo robot walk with ROS – #Part 2](https://www.theconstruct.ai/wp-content/uploads/2018/02/Making-Aibo-robot-walk-with-ROS-Part-2-screen.png)
![[ROS Projects] – Performing LSD-SLAM with a ROS based Parrot AR. Drone](https://www.theconstruct.ai/wp-content/uploads/2018/01/Performing-LSD-SLAM-with-a-ROS-based-Parrot-AR.-Drones.png)
![[ROS Projects] – Exploring ROS using a 2 Wheeled Robot](https://www.theconstruct.ai/wp-content/uploads/2018/01/EXPLORING-ROS-USING-A-2-WHEELED-ROBOT.png)
