Good Wheel Hunting: Umass Lowell's Scavenger Hunt Robot System

    Indoor scavenger hunt. Scavenger hunt was a judged competition where robots autonomously searched forindoor environment independent of any higher-level task. in our system, a. robotics.cs.uml.edu.

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Good Wheel Hunting: UMass Lowell’s Scavenger Hunt Robot System
Robert Casey, Andrew Chanler, Munjal Desai, Brenden Keyes,
Philip Thoren, Michael Baker, and Holly A. Yanco
Computer Science Department, University of Massachusetts Lowell
One University Avenue, Olsen Hall
Lowell, MA 01854
{rcasey, achanler, mdesai, bkeyes, pthoren, mbaker, [email protected]
Abstract
This paper describes the UMass Lowell entry into the
Scavenger Hunt Competition at the AAAI-2005 Robot
Competition and Exhibition. The scavenger hunt entry was
built on top of the system we have been developing for
urban search and rescue (USAR) research. The system
includes new behaviors and behavior sequencing, vision
algorithms and sensor processing algorithms, all used to
locate the objects in the scavenger hunt.
INTRODUCTION
The AAAI-2005 Scavenger Hunt required a unique
combination of artificial intelligence, human-robot
interaction (HRI) and computer vision techniques. The
scavenger hunt was a judged competition where robots
autonomously searched for a predefined checklist of
objects in a dynamically changing area crowded with
people. The objects ranged in difficulty from simple (e.g.,
a bright yellow beach ball) to complex (e.g., a multicolored
soccer ball and a plush dinosaur doll). Many of the objects
shared colors with objects in the environment as well as
each other, making it more difficult to accurately identify
the objects. To increase the difficulty of the task, teams
could also sequence behaviors to find multiple objects in
succession without human intervention.
Our scavenger hunt entry drew upon several research
projects from our lab. The robot has been developed for
research in remote operations such as urban search and
rescue (USAR), covering topics such as human-robot
interaction, robust robot control and real-time vision
processing. The competition provided an opportunity to
build these projects into a single system.
ROBOT HARDWARE
Our system’s platform is an iRobot ATRV-Jr, a rather
large, four-wheeled all-terrain research platform initially
equipped with a full sonar ring (26 sonar sensors), a SICK
Copyright © 2002, American Association for Artificial Intelligence
(www.aaai.org). All rights reserved.
laser rangefinder, pan/tilt/zoom camera, and a full Linux
(kernel 2.2) box running on an Intel Pentium 3 processor.
Since purchasing this robot over three years ago, we have
heavily modified its hardware and software for our
research projects.
Figure 1: Customized iRobot ATRV-JR
One of our research projects is the development of
effective human-robot interaction for remote robot
operation. While studying a large number of interfaces
developed for the AAAI and RoboCup USAR
competitions, we noted that a large percentage of the robot
hits in the environment were directly behind the robot
[Yanco and Drury 2004]. The reason for this problem was
clear; anything outside of the 180° camera pan range
directly in front of the robot was essentially a blind spot.
To alleviate this problem, we added a rear-facing
pan/tilt/zoom camera to our robot. This camera (a Canon
VCC4) is identical to the forward-facing camera that was
already in place. In user tests using the two cameras, we
noticed a significant reduction in the number of times users
drove into obstacles behind them.
The next, and possibly most significant change we made to
the system itself was to fully upgrade the system's on-board
computer. While studying previous competitions, the need
for more advanced vision processing became evident.
Figure 2: The interface developed for remote robot operations, described fully in [Baker et al. 2004]. This
interface was modified for use in the scavenger hunt competition.
To alleviate the computation requirements of these
advanced algorithms, we replaced the Pentium 3 system
with a Pentium 4 system with one gigabyte of RAM. For
this upgrade, the robot required a new power supply and
updating of all of its software, including iRobot’s Mobility
software.
INTERFACE OVERVIEW
The interface used in the competition was developed in our
lab, initially for USAR applications [Baker et al. 2004].
Our design was influences by studying over twelve USAR
robot systems over the past three years at AAAI and
RoboCup competitions. The interface was designed to
improve situation awareness. We have observed that the
main area of focus for all users is the primary video screen
to the exclusion of all other useful status information. As a
result, many of the elements are displayed in or around the
video window. For example, we overlay a crosshair to
indicate the pan and tilt orientation of the primary video
camera. We also include a video stream from the back
camera that is designed to mimic the look and feel of a
car’s rear-view mirror. The ranging information is situated
around the main video screen, so that users do not need to
look far from their main focus to see if they are close to
obstacles that may not be in the camera’s view. A
dynamically generated map is also placed directly to the
right of the main video screen. For more details about the
design of the interface, see [Baker et al. 2004].
The interface communicates wirelessly with the robot
using the User Datagram Protocol (UDP). The robot is
constantly broadcasting its status information, including
ranging information, video feeds, and battery level. These
updates are sent both on a regular basis as well as after
receiving a command from the interface. The interface
updates its displays only after acquiring new status data.
We do not use the Transmission Control Protocol (TCP) in
this system for two main reasons. When using TCP, if
communications are lost or messages are sent at a greater
rate than the back end can handle, the commands will
queue up. We do not want queuing because when
communication resumes all those commands will be sent to
the robot, which could cause it to behave erratically for a
period of time until all the queued messages are dealt with.
This type of operation could cause damage to the robot and
environment, as well as put humans at risk. In these cases
it is safer to drop packets rather than queue them up. Also,
we do not use TCP because in a congested network or with
a poor wireless signal the bandwidth will be throttled down
to try to relieve some of the congestion. However, we
always want the highest possible bandwidth for the
interface/robot communications, which allows for more
effective control.
For this competition, the only modification we made to the
interface was the addition of a “Behaviors” menu, showing
in figure 5. This menu is dynamically created from a list of
behaviors sent by the robot when the interface is started.
This menu included behaviors such as Follow Green Trail,
Track Soccer Ball, and Track Yellow Ball. Using this
menu, the user can choose to activate/deactivate as well as
queue up any number of behaviors to be performed in
sequence. The actual details of the behavior architecture
are discussed below.
NAVIGATION
For the scavenger hunt, our robot was required to navigate
safely in a conference hall setting in the presence of
moving objects and/or people. Since the rules stipulated
that AI techniques must be used in each entry, we
developed autonomous navigation behaviors that could be
used to move the robot safely around a semi-structured
indoor environment independent of any higher-level task.
In our system, a mode is a collection of concurrent and/or
sequenced behaviors that accomplish some high-level task.
A behavior is a lower-level response to environmental
stimuli such as obstacles and colors. This section describes
the mode/behavior system we designed for safe
autonomous robot navigation at the competition.
Navigation Behaviors
Each behavior has a weight that specifies its contribution to
the overall behavior. That is, the actual behavior (drive
command) executed by the system is a blending (weighted
sum) of all active behaviors. All behaviors can be
activated and deactivated dynamically as the task or
situation dictates. It is also possible for a behavior to
suppress or partially suppress one or more other behaviors.
Using weights and suppression, it is possible to achieve
very sophisticated behaviors using very simple building-
block behaviors. Below, we describe the behaviors in our
system.
Joystick Behavior. This behavior is the user’s raw
joystick command. The analog joystick command is
turned into a drive command that varies in magnitude with
how far the joystick is pushed or pulled.
Spin Behavior. This behavior causes the robot to spin or
rotate in place. We created this behavior to search for an
object, such as the yellow beach ball, 360 degrees around
the robot. Since our cameras can not pan more than 180
degrees, we decided this behavior would be the quickest
and simplest way to locate an object in wide open space.
Stuck Behavior. Since our robot has a full sonar ring, we
can detect obstacles equally well on all sides of the robot.
This means that our robot can travel just as safely
backwards as it can forwards. We take advantage of this
ability in our stuck behavior. If the robot is navigating
autonomously, and it has not made progress—determined
by how much it has moved in set period of time—then the
robot starts driving in the opposite direction. This
approach has proven successful because it is highly
unlikely that the robot could become blocked on the front
and rear sides simultaneously.
Open Space Behavior. Using the full sonar ring the open
space behavior implements the idea of a potential field by
treating individual sonar readings as vectors emanating
from the robot and computing the vector sum of all sonar
readings. The resultant vector gives the direction and
speed of the robot, which will be toward the most open
space. This behavior is not very useful by itself because
cancellation of vectors (vector sum equals zero) is possible.
There are many possible orientations of the robot with
respect to obstacles in the environment that cause
cancellation of vectors, which prevents the robot from
moving. However, when combined with other behaviors,
this can be a helpful behavior.
Forward Behavior. This is a very simple behavior that
causes the robot to drive straight ahead. As explained in
open space behavior, the potential field method turned out
to be unsatisfactory for moving the robot around in a
hallway environment, so we added this behavior.
Stop Forward Behavior. This is another simple behavior
that completely suppresses forward behavior when there is
something very close to the front of the robot. Without this
protective behavior, the combined forward tendency of the
other behaviors could cause the robot to hit something in
front of the robot. This behavior prevents front hits very
effectively.
Avoid Front Wall Behavior. We created this behavior to
deal with the situation where the robot is moving in open
space and encounters a barricade or wall on the front. The
robot will turn left or right depending on the angle of the
robot with respect to the wall. If the robot is facing more
to the left, the robot will turn left. Once the robot has
Figure.3: Shared mode achieves safe, autonomous navigation in a semi-structured indoor environment.
aligned itself parallel to the wall, the wall following
behavior would kick in and take over.
Wall Follow Behavior. This behavior detects walls on
either side of the robot and follows the closer wall in the
case of a hallway. The robot maintains a specified distance
parallel to the wall it is following by making steering
corrections whenever it deviates from the specified
distance. The robot makes steering corrections in
proportion to the amount of deviation from the preferred
distance. In this way, the corrections are generally very
gentle and the robot maintains a fairly straight and constant
path parallel to the wall.
Corner Behavior. This behavior detects when the robot is
in a corner and guides the robot past the corner by turning
the robot in the appropriate direction. Corner is used in
conjunction with the wall follow behavior to achieve
autonomous navigation in a hallway environment.
Safe Mode. Safe mode is really a speed governor mode in
the current design of the system. Safe mode was created
originally to allow the robot to protect itself when a user
command would result in bumping into an obstacle. In
safe mode, the robot detects nearby obstacles in the robot’s
path and stops the robot before a collision can occur.
When the robot is navigating autonomously, however,
stopping the robot would impede the robot’s progress.
Instead, we limit the robot’s speed in proportion to its
distance from the nearest obstacle in the direction of
motion. So the robot slows, but continues to make gradual
progress. The idea behind allowing the robot to make slow
progress is that, eventually, some environmental feature
(open space, a wall, a corner, etc.) will be detected and
trigger a behavior that will move the robot away from the
obstacles that are slowing it down.
Shared Mode: A Complete Mode/Behavior
Example
This section describes a general navigation mode in our
robot system: shared mode. Shared mode encompasses a
true blending of robot and human control by combining
user joystick commands with the other behaviors. The
weight of the joystick behavior controls how much the user
may override or influence the robot’s autonomous control.
As shown in figure 3, shared mode takes user joystick
commands and sonar readings as inputs. Each of the active
behaviors that make up the shared mode can contribute a
drive command, but one behavior can suppress one or more
other behaviors depending on the desired overall
Figure 4: Demonstration of the door detection algorithm.
behavior. In the current example, the wall follow behavior
can suppress the avoid front wall behavior, and the corner
behavior can suppress wall follow and avoid front wall.
The drive commands from the various behaviors are
summed according to the weight of each behavior and the
blended command is passed through a speed-limiting
function in safe mode, which determines the command that
actually moves the robot.
DOOR DETECTION
The door detection algorithm uses the laser ranging data to
detect doors. The data from the laser range sensor is
collected in the form of x,y coordinates rather than vectors
to obstacles. The algorithm, on detecting an opening,
calculates the probability that the opening is a door and
indicates the location of the door relative to the robot. The
algorithm is capable of detecting multiple doors at the
same time.
A door is defined by three values: the minimum and
maximum width, as well as the minimum depth of the
door. The depth of a door can be defined as the space
behind the door that must be free of objects. Thus, this
algorithm can detect open doors only at this time.
The algorithm reads x,y values and records the coordinate
value if the difference between the current and the last y
coordinate is greater then the defined minimum depth. This
is assumed to be the starting coordinate of the door, which
we refer to as an upward spike. From this point on if
another upward spike is observed then it automatically
becomes the starting coordinate of the door, in other words
the new upward spike.
While reading the values, if the difference between the
current and the last y coordinate is less then the negative of
the defined minimum depth and an upward spike has
already been found, it is recorded as a downward spike. If
the distance between the last recorded upward spike and
the current downward spike satisfies the minimum and
maximum width constraints, a door is detected.
Once a downward spike is found the algorithm resets the
coordinates for the upward spike, and so the algorithm
starts to look again for an upward spike. Once a door has
been found, the algorithm calculates the probability of that
being a door. The algorithm uses a simple but effective
method to do so. The angle made by the perpendicular
drawn from the center of the door towards origin and line
segment from the mid-point of the door to the origin is
found. Higher differences lead to lower probabilities for
the detected door being an actual door.
Overall this is a fairly robust algorithm. During the tests
performed in the hallway near our lab, doors were detected
around 80% of the time. However, the algorithm performs
poorly when there are many tables and chairs placed close
to each other, as they were in the competition area. We are
working on ways to improve this. One of the planned
improvements is to make sure that there is a specified
distance before and after the detected door with the same
slope as that of the door, indicating walls on both sides.
In the figure 4, the small rectangle in the center of the
screen is the robot and the vertical line indicates the
forward direction. The lines around the robot indicate the
environment surrounding the robot as seen by the laser
range sensor. The dashed line seen in front of the robot is
the door which has been detected. The two points where
the minimum depth lines are crossed correspond to the start
and the end of the door.
Figure 5: The behavior menu.
TASK SEQUENCER
Once we had the ability to complete various tasks in the
scavenger hunt, we wanted a way to combine the tasks for
the competition. Our solution, a behavior sequencer,
allows us to queue up several tasks for the scavenger hunt.
For example, we can queue up Find Door, Follow Trail,
Find Yellow Ball, and Find Pink Bowl; the tasks will the be
executed in that order.
Our interface supplies the functionality to start, stop, and
pause the sequencer or even force a transition to the next
task. The interface also allows us to manipulate the
sequencer queue by adding, removing, and clearing tasks.
The sequencer queue supports multiple instances of the
same tasks, which allows us to run the same behavior
multiple times in a row. With this we were able to follow a
colored trail, find various colored objects (one at a time),
and then follow the trail back home.
The sequencer gave us more flexibility during the judging
of the system. We could ask the judges the order they
would like to see our robot perform the tasks rather than
having a hard coded predefined scavenger hunt, allowing
the judges to challenge our system.
VISION SYSTEM
Our entry used a vision system called Phission, developed
in our lab. Phission is a toolkit to assemble any number of
multi-threaded subsystems for continuous vision data
processing. There are modules for capturing, processing,
and displaying video data. There is also a system module
that provides a simplified interface for starting, pausing,
and stopping all the threads that each module encapsulates.
Any number of modules may exist within a system module
and the only limitation lies within the capabilities of the
hardware.
Using a C/C++ API, the output of one module is connected
to the input of another module. A capture module output
will be connected to the input of a processing pipeline
module or a display module. The processing module also
can be connected to a display module. The display module
is mainly used for debugging and development of the
processing system because of the resources required to
display video data. Generally, the display will be disabled
during a competition or live run when seeing the video is
not necessary.
Phission is capable of processing video at very high frame
rates because of the way in which the modules pass video
frames to each other. While the pipeline thread is
processing a video frame, the other threads continue to
capture and display video frames. The capture module
always has a copy of the most recent, fully captured video
frame. When the processing thread is ready to process
another image, it can retrieve the newest frame without
having to wait for a capture cycle to complete. A display
connected to the output of a processing thread will also be
able to copy the most recent fully processed image as soon
as it's ready. Phission was designed in this way to transfer
data asynchronously in a passive manner. A source
module does not push data to a destination module. In
Phission, any given module is not likely to wait as long for
the output from another module as compared to a non-
threaded serially designed system.
The Phission pipeline module is a simple one input to one
output processing thread. Filters are added into the
pipeline and they use a workspace image as input and
output. The workspace image is managed by the pipeline
module. Its purpose is to remove the need for a filter to
copy an input image and copy to an output image every
time the filter is run. All filters have a common high level
interface by inheriting from a filter class.
While Phission was built with video processing as its main
data type, the system is extensible to any type of data
through a parent object that provides for the
live/continuous updating of a data object. The blob and the
histogram filter both make use of that live object interface
to store and propagate histogram and blob information. A
control thread, usually the main loop of a program, can
connect to a blob data object that is output from a specific
blob filter and update the object when new data is
available. In the case where new data is not available, the
control loop can pass over the processing of the blob data
until new data is available.
Phission is available as a library that can be built on Linux
or Cygwin/Windows and linked with any C/C++ program.
For languages such as Java or Python, the Phission API
Figure 6: (a) Histogramming yellow ball and (b) HSV Threshold
and blobbing yellow ball. (c) Histogramming the pink Bowl and
(d) HSV Threshold and Blobbing of the pink bowl. (e)
Histogramming a sheet of green paper on the floor and (f) HSV
Threshold and blobbing of the green paper laid in a trail.
and facilities can be included through a loadable module
that is installed on supported systems.
OBJECT RECOGNITION AND TRACKING
Most of the competition tasks involved finding various
colored objects. We created a generic behavior capable of
tracking a colored object and driving up to it. Specific
object tracking behaviors (e.g., Find Yellow Ball, Follow
Green Trail, etc.) are created with a set of saved color
matching thresholds which are input to the generic object
tracking behavior. The Follow Green Trail behavior is
derived from the Track Object behavior to allow for a
minor difference in control functionality. Where the Track
Object behavior will stop when the object is directly in
front of the robot, the Follow Green Trail behavior will
continue driving while it sees another green marker that is
large enough and below the horizon.
In order to achieve better vision processing performance,
the capture module is started up when the robot back end
program is loaded. A reference to the capture module is
passed to each Track Object behavior upon creation.
Whether the robot is running a behavior or not, the capture
module will always be running to provide the very latest
image immediately when the behavior is activated.
Training and segmentation on video is done using the HSV
(Hue Saturation Value) color space. The HSV color space
is better because it is less sensitive to lighting changes and
colors are more easily matched. Matching a color in HSV
space proves easier because similar colors (for example, all
yellow objects) are located in a single linear range within
the Hue field. In addition, removing bright and dark objects
can be done more easily given HSV color data. This
eliminates much of the video data that is useless.
The trained values for an object are found by using all the
same hardware equipment and pre-processing filters
(Gaussian, HSV conversion) and then running a histogram
over an area of the image that contains the object. The
histogram information output is an HSV color value and an
upper and lower threshold value. A program is used to
histogram on a live video stream as opposed to single
frames loaded from files. In this manner, one can see how
the blobbing differs from frame to frame and it also allows
the placement of other objects within the field of view to
determine the potential for accidentally matching other
objects. The same program can take in an HSV threshold
parameter file to permit live configuration of the HSV
threshold value to remove much of the unwanted
surrounding scenery. Examples of the images seen during
training are shown in figure 6. The images on the left are
the histogram input images. The images on the right are the
images that are first HSV thresholded and then blobbed on
the histogram color value outputs.
The object matching algorithm is rather simple at a high
level. First the video frame is blurred with a 3x3 Gaussian
filter to reduce image noise and inconsistency. Next, the
image is converted to the HSV color space. After
conversion, the HSV values are thresholded according to
their Saturation and Value bytes to remove very bright or
very dark pixels. Finally, the blob filter is run to segment
HSV pixels that match the given trained values.
After all the processing of the video data, the location of
the objects within the video frame can be retrieved and
acted upon. Using an assumed horizon located at the
middle of the image, the tracking behaviors will align on
largest blob below the horizon until it is in the center of the
camera's view by returning rotation motor values and no
translational motor values. When the image is within the
center of the image, the behaviors will return translational
values to drive towards the object. The normal behavior is
to stop when the object is reached, however, the follow
trail behavior will continue to drive as long as there is a
large blob that matches the trail object.
Since all this happens in conjunction with many other tasks
and sensor processing, it is possible to poll on whether
there is new blob information available. If there is no new
blob information available, the previous rotational and
translational values are returned and are not updated until
new information is ready. This prevents the blocking of
more important avoid behaviors and other general control
routines. The vision subsystem aggregates the video data
into a slower updating data set (blob coordinates and size
information) which takes time to generate. Waiting on this
data can adversely affect the response of other robot
behaviors in the control system.
RESULTS
Our system was able to locate several of the scavenger hunt
items, including the yellow beach ball, the pink bowl, the
two-colored soccer ball and a trail of green paper. Work
on distinguishing the pink and orange dinosaur from other
pink and orange items was not completed in time for the
competition.
The action sequencing developed at the competition
allowed for the system to demonstrate all of its finding
behaviors in an order specified by the judges.
The robot performed its tasks well, despite a last minute
charging circuit failure, resulting in discharged batteries
just a half hour before the time set for judging.
Fortunately, we were able to bypass the circuit to allow the
batteries to get some charge before the judges arrived.
Our system was awarded two technical awards: one for its
object recognition capabilities and another for the usability
of the control interface.
ACKNOWLEDGEMENTS
This work was supported in part by NSF IIS-0308186, NSF
IIS-0415224 and NIST 70NANB3H1116. Travel support
was provided by the sponsors of the AAAI Robot
Competition and Competition.
REFERENCES
Michael Baker, Robert Casey, Brenden Keyes and Holly
A. Yanco (2004). “Improved Interfaces for Human-Robot
Interaction in Urban Search and Rescue.” Proceedings of
the IEEE Conference on Systems, Man and Cybernetics,
The Hague, Netherlands, October.
Holly A. Yanco and Jill Drury (2004). “‘Where Am I?’
Acquiring Situation Awareness Using a Remote Robot
Platform.” Proceedings of the IEEE Conference on
Systems, Man and Cybernetics, The Hague, Netherlands,
October.