CS 309: Autonomous Intelligent Robotics FRI I Lecture 17: Starting - - PowerPoint PPT Presentation

CS 309: Autonomous Intelligent Robotics FRI I Lecture 17: Starting the Robot Coordinate Frames TF2 Forming Project Groups Instructor: Justin Hart

http://justinhart.net/teaching/2019_spring_cs309/

Pick a BWIBot

V2 V4

V2 Startup

• Find the back of the robot

– Yes, the screen faces the BACK

V2 Startup

• The switch should be in the “charge” position

when you find the robot.

V2 Startup

• Set the switch to the neutral position

V2 Startup

• Unplug the charging cable
• Set the switch to the “battery” position

V2 Startup

• Go to the front of the robot

V2 Startup

• Press the green button.

– It should light up.

V2 Startup

• Press the yellow button.

– Yellow button and blue indicator light should light.

V2 Startup

• Hit the power button on the laptop

V4 Startup

• The V4 is different

– This is actually the front of the robot.

V4 Startup

• Disconnect the power supply.

– First, undo the screws on the connector

V4 Startup

• Disconnect the power supply.

– Then disconnect the plug.

V4 Startup

• Locate the Emergency Stop and Power Button

– On the top at the front of the unit on the V4

V4 Startup

• The Emergency Stop may be depressed

– If it is, gently twist it to the right and it will lift.

V4 Startup

• Press the power button.

V4 Startup

• It will illuminate.

– This indicates that the robot is now on.

V4 Startup

• Reach back to the right inside the chasis.

– You can feel the power button to turn it on.

V4 Startup

• Note that the V4 has a wireless keyboard

– If the batteries are dead, obtain assistance

System Startup

• From here, both systems work the same

System Startup

• Select Ubuntu on this screen using the arrow

keys and hit “Enter” or wait for boot

System Startup

• Log in as FRI

– Using the FRI password. A mentor can help you.

Doing your homework

• For your homework, you will do this on a real

robot.

• These screenshots come from doing this in

simulation.

– Installing the simulators is easy if you want to try. – Installation instructions for BWI here:

• https://github.com/utexas-bwi/bwi

– Tutorials on running the robot & simulators here:

• https://github.com/utexas-bwi/documentation/wiki/Software

System Startup

• Open several terminals
• cd catkin_ws; source devel/setup.bash in each.

System Startup

• roscore

System Startup

• Startup your robot

– roslaunch bwi_launch segbot_v2.launch (or v4)

Pick your floor

• A screen will pop up asking what floor you are
• n.
• The AI floor is the 3rd floor, and you are almost

certainly there at this step of the process.

Next, you want to move the robot

• The next step is to move the robot into the

hallway, where you want to start.

• I generally do this before localizing the robot,

just to get it out of people’s way.

System Startup

• Rosrun segbot_bringup teleop_twist_keyboard

System Startup

• Slow down the robot before moving. Hit z.

Next, you want to move the robot

• The robot will move quicker than you expect.
• Press the ‘k’ button to stop its motion.

System Startup

• Localize the robot.
• Hit “2D Pose Estimate”
• Click on where the front of the robot is on the map

in rviz (the screen is on the BACK), and drag the cursor FORWARD.

– Note that the robot graphic may not be there, because

the real robot does not know where it is yet!!

• A green arrow will appear showing what you believe

the robot’s pose to be.

System Startup

• The system uses a probabilistic method to find

the robot’s pose, so your localization just aids in this process.

• Instruct the robot to move a bit by using “2D Nav

Goal”

• The robot’s localization will improve as it moves.
• 2D Nav Goal works the same as “2D Pose

Estimate”

System Startup

• rosrun bwi_tasks visit_door_list

System Startup

• The robot will now start driving around visiting

the doors in the hallway.

System Shutdown

• ctrl-c in the terminal to kill visit_door_list
• teleop_twist_keyboard to drive back into the lab
• Do the start-up steps in reverse for powering on

the robot in order to power off

• Plug back into the charger, and go back into

charge mode

TF Classes & Functions

• tf::TransformListener listener

– Special class that listens on behalf of the TF library

so it can compute transforms between frames.

• tf::StampedTransform transform;

– A spacial transformation

• Listener.lookupTransform("odom", "base_link",

ros::Time::now(), transform);

– Looks up the transformation “base_link” into “odom”

One more..

• listener.waitForTransform("odom", "base_link",

ros::Time(0), ros::Duration(4));

– Wait for the transform to be available – TF may not have heard enough data to make this

transform work, and it will throw an error in that event.

You can also send frames

• tf::TransformBroadcaster br
• br.sendTransform(

tf::StampedTransform( transform, ros::Time::now(), fromFrame, toFrame));

• StampedTransform - simply adds a timestamp to the transform
• ros::Time::now() - makes that timestamp now
• fromFrame, toFrame – The names the the frames.

Poses & Transformation

• Pose

– The position and orientation of an object in space – Generally expressed as a position and a rotation matrix into the

correct pose

• Transformation

– The relationship between two poses – Generally expressed as a translation (a position) and a rotation

• ROS has types for both!

– But they contain basically the same data

Poses & Transformation

• geometry_msgs::Pose pose

– pose.position

• pose.position.x (y,z)

– pose.orientation

• pose.orientation.x (y,z,w)

– Orientation is expressed

as a quaternion

• We have software that

handles it.

• tf::Transform transform

– getOrigin()

• getOrigin().x() (y(), z())

– tf::Vector3 origin(x,y,z) – setOrigin(origin) – getRotation() – tf::Quaternion q(x,y,z,w) – setRotation(q) – The tf::Quaternion class also

supports

• Euler angles
• Axis & angle

Example: In simulation

• roscore
• roslaunch bwi_launch simulation_v2.launch
• If we select “Global Options” on the left, we can

change “Fixed Frame” to base_footprint, telling us to move rviz so it is focusing on the robot

TF view_frames

• rosrun tf view_frames

– This will generate a PDF of the current TF tree

A few of these frames

• BWI uses a special map service to tell the robot which floor it is
• n
• “map” is our “global frame,” sometimes called the “inertial frame”

– It is the top level, all frames are relative to it

• “3rdFloor_map” and “level_mux_map” are from the BWI map

service.

– You can safely ignore them

• “odom”

– Short for “odometry.” – When we track the robot’s motion, it is relative to “odom.”

Where is the robot?

• odom

– The robot’s motion is tracked relative to odom – odom is generally in a fixed position

• base_footprint

– Where the robot is – Remember, base_footprint is at (0,0,0) in

base_footprint’s frame

– So we compare to odom to know where the robot is

Example

• We can watch this from the command line
• rosrun tf tf_echo /base_footprint /odom
• rosrun segbot_bringup teleop_twist_keyboard
• If we watch tf_echo, we can see the translation changing.

– This is how our system knows where the robot is!

More frames

• The robot has MANY frames
• base_link

– The physical base of the robot’s mechanics

• Rather than base_footprint, where it is on the floor
• Decoupling the two simplifies modeling
• chasis_base_plate_link

– Where parts are mounted on the BWIBot

• laser_obstacle

– Where the laser sees the nearest obstacle

• caster_wheel_link, base_link_left_wheel, base_link_right_wheel

– The robot’s wheels

AR Tags

• Let’s take a look at tracking an object and

finding its coordinate frame.

• Alvar

– An open source library for tracking AR Tags

Virtual Reality

• The simulation of a

3D world that can be experienced first- hand

• 3D

– Video games – Virtual worlds – Headsets

Augmented Reality

• Adds 3D content to

images and perception of the real world

• The photo on the right

merges real content and virtual content in a real-estate application

Augmented Reality Toolkit

• What is an AR Tag?
• Augmented Reality Tag

– The image on the right

comes from a demonstration of Augmented Reality Tool Kit

– Provides a coordinate

frame to render 3D content

• nto

– We will use a similar

package called Alvar

How does this work?

• Recall our transforms

– Translation – Rotation

• Augmented Reality uses projective

transformations

– Homographies – Projections

Project Group Formation

• This group will

– Do homework 5 together – Do homework 6 together – Do the final project together

• Take 15 minutes to choose a team

– Email me you + your teammates + your EIDs and

email addresses, 1 per team

For homework 5

• Work out the Direct Linear Transformation

(DLT) of a homography to resolve its position in space

– Just kidding

• From our perspective, AR Tags provide us with

– Translation – Rotation – And work with TF2

ar_track_alvar

• ROS package for using AR Tags!
• Needs to be configured for your camera

– This happens in a .launch file

• Generate AR Tags
• Print them
• Attach them to a rigid surface
• Your system can now track an AR Tag
• Example