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Wireless Group Manipulation of Autonomously Guided Mobile Robots for Smart Living Space Applications
M.-H. Chen, D. Gu, Y.-D. Fu, C.-H. Pi, K.-S. Ou, and K.-S. Chen Department of Mechanical Engineering, National Cheng-Kung University, Tainan, Taiwan
(Tel: +886-6-2757575 ext.62192; E-mail: [email protected] )
Abstract: In this work, by integrating omni-wheel mobile robots with X-Bee communication protocol, Arduino control,
IR range finders, and CMOS camera, as well as wiimote multi-zone localization, tasks such as obstacle and collision
avoidances, following, autonomously movement, and indoor localization of group robots are implemented as the first
step toward an autonomously control of group robots for smart living space applications. In conjunction with hardware
design, novel algorithms are developed for successfully realizing and demonstrating these tasks. With these key issues
being solved, more realistic scenario can be designed for achieving the real group robot applications for indoor service
in the future.
Keywords: Autonomously Guided Motion Control, Mobile Robots, Arduino, X-Bee, Group Manipulation
1. INTRODUCTION To understand and utilize the advantage of group
movement and cooperation of biological system are long
term pursuit by researchers in different aspects. For
robotics and artificial intelligence fields, by coordinating
and cooperating of large individual robots, complicate
tasks can be fulfilled. In addition, through proper
networking and communication, these robots can
exchange their information and learned from peers and
the environment for tackling more complicated tasks,
which cannot be done by single or ungrouped robots.
Consequently, it is possible to gain particular advantages
by applying the bio-inspired nature into the field. As a
result, several research laboratories have been deeply
engaged into the study for autonomously manipulation of
robot groups for specific applications by investigating in
virtually all aspects of mobile robots including remote
motion control, navigation, and speech recognition.
Previously, Rooker and Birk [1] utilized wireless
communications between two robots to establish
environmental map for robot manipulations. The ability
to manipulate a robot group enables new applications for
automatic indoor landscape arrangement and human
machine interactions, or for the smart architectural
technology; it might require coordination and group
manipulation of multiple mobile robots. This work is
inspired by the behavior of natural biological animal
groups, in which the individual member could either have
their own intelligent and information to perform motion
and decision making based on their own information or to
exchange information and perform group motion for
finishing a cooperative task. The bio-mimic approach
could potentially leads to a large scale integration of
robotic members to form a massive group for performing
complicated tasks.
In order to pursue the goal mentioned above, several
important fundamental tasks must be established and
realized. These fundamental issues including: collision
avoidance, global localization, obstacles avoidance, and
group manipulations. For obstacle avoidance, most
widely used technique is to utilize the information
observed by external-mounted CCD cameras for
subsequent trajectory planning [2,3] or by sensor
mounted on robots themselves [4]. Both approaches have
their own advantages and disadvantages. For example, the
former approach can obtain the global status of the robot
group but it cannot mimic the autonomous behavior of
individual elements. On the other hand, the latter one can
SICE Annual Conference 2011September 13-18, 2011, Waseda University, Tokyo, Japan
PR0001/11/0000-2143 400 2011 SICE- 2143 -
provide information of individual robot for decision but
the lack of global coordination makes it difficult to
perform tasks related to global positioning efficiently.
As a result, in this work, we combine both approaches by
utilizing multi-zone wiimote localization technique
developed by us [5] for global information assessments
and by IR range finders and CMOS cameras for recognize
the nearby objects. Based on the hardware and the
subsequent software implementation, it is possible to
achieve basic ability such as recognition, following [6],
obstacle avoidance, and global positioning. With these
basic abilities, it is possible to demonstrate more
dedicated group autonomous manipulations similar to the
previous work in robotic fish [7] in smart live
applications.
2. SYSTEM SETUP Four omni-wheel type mobile robots designed and
built by us are shown in Figure 1. These omni-wheels are
driven by three S03T_STD servo motors (powered by a
battery set) with an(UNO MEGA2560) Ardunio control
card with a clock 16 MHz and a (XBee ZNet2.5) X-Bee communication protocol (based on IEEE 802.15.4 with
maximum data rate 250kpbs) for the basic motion and
communication ability. The Arduino card sends PWM
signal between 0 and 255 to control the wheel rotating
speed (i.e., and the global translating ( x , y ) and self-rotating velocities ( ) of those robots can be obtained by the following kinematics equation:
0
,
where R and L are the radii of the wheels and the robot,
respectively. 1 is the motion direction, 2 is 1 to indicate forward or revise rotations. g1 and g2 are velocity gains.
Furthermore, depends on the application, associated
control laws to govern the relationship between the
feedback information and the controlled are also
established for performing subsequent tasks.
(a) (b)
(c) (d)
Fig. 1 The omniwheel mobile robots (a) key units, (b) the
fleet, (c) schematic plot to show key parameters, and (d)
IR range finder and CMOS camera assembly
In addition, a mesh topology between the host and
these robots is established by utilizing the X-Bee protocol.
The host sends sets of string signals in broadcasting
manner, which are received and decoded by each robot to
recognize the specific parts for their own. The mesh
topology enables us to overcome the possible signal
attenuation and delay due to the presence of obstacles.
Next, based on different task planning, each robot
equips three sets (one main and two auxiliary sets) of
(Sharp GP2Y0A21) IR range finder shown in Figure 1d
for detecting nearby objects for obstacle avoidance. The
effective ranges for the main and these two auxiliary IR
range finders are 0.15-5m (for a scanning angle 170) and 10 80cm (for a scanning range 240), for coarse and fine range determination, respectively. These range
finders are scanning back and forth for detecting possible
obstacles in a wide range. A novel algorithm is also
developed to coordinate the information from IR range
finders and the scanning angles for determining optimal
trajectory for obstacle avoidance.
Furthermore, each mobile robot also equips different
design of IR LED patterns and a CMOS IR camera
detached from Nintendo wiimotes for sensing the distance
and orientation between omni-wheels and for the purpose
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of object recognition. Next, wiimotes and their
corresponding IR LEDS are also installed on the ceiling
and the robots for globally monitoring the absolutely
location of these robots. In together with the multi-zone
localization technique developed by us [5], this provides
the global position information for external supervisor on
future task planning and control. Finally, the interaction
between the robot group and the external human
supervisor is established between the host computer and
either the master robot (group mode) or all robots
(individual mode) by using a graphic user interface
written under a LabVIEW environment. The entire
functional block diagram is shown in Figure 2.
Fig. 2 The functional block diagram of the entire work.
3. EXPERIMENTALSETUPAND ALGORITHMS DEVELOPMENT
Various fundamental benchmark problems have been
experimentally demonstrated to allow us to evaluate the
overall performance and to examine the possible faults in
recognition and communications between objects, as well
as to refine the manipulation schemes. By such efforts,
the above mentioned tasks are successfully demonstrated.
The performance measured and lesson learned could be
very valuable for future large scale integration.
Obstacle avoidance:
As mentioned earlier, an algorithm is proposed to
perform the obstacle avoidance. The algorithm is briefly
illustrated in Figure 3, the mobile robot is initially motion
in a straight manner and the IR range finders scan with a
rotating speed approximately 33.33 rpm to continuously
search for possible obstacles. The scanned area was
divided into four zones and the coordinates of the
possible obstacles are (di, i) (i=1~4). If there are any di less than its pre-defined threshold value dti, the obstacle
avoidance mode is then activated. A weighting index pi
(i=1~4) is then assigned based on di and it reflects the
obstacle presence distributions. Based on pi and i, the algorithm determines the velocity gains g1and g2 and the
possible orientation modification for the next step. By
such a dynamic scanning manner, the trajectory of the
robots will be evaluated and updated after each motion
step. Previous work[4] used ultrasonic sensor for obstacle
detection but it cannot precisely determine the obstacle
location and the robots would be difficult for making
following up decisions. On the other hand, this approach
can precisely determine the obstacle location and
promptly response it during the next step.
Fig. 3 Brief flowchart of the obstacle avoidance
algorithm.
Following:
As mentioned earlier, each robot equips with a set of
IR LEDs and a CMOS IR camera with a resolution of
1024768 for detecting the presence of nearby robots. By recognizing the image pattern of IR LEDs, each robot can
distinguish its neighborhood. Also, by evaluating the
coordinates of these LEDs, it is possible to calculate the
relative distance and orientation between two robots and
performing following motions. A following algorithm is
developed and briefly illustrated in Figure 4. The first
phase is to search the master robot. We divide the visible
area of the CMOS camera to five zones, denoted as qi,
i=1~5. Meanwhile, the motor scanning angles are also
partitioned into three different sets j, j=1~3. Hence the
total visible area is organized as 15 zones and a weighting
index Cij is assigned for each zone. Once an object is
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detected in a zone, the corresponding index is switched
from 0 to 1. Depends on the distribution of Cij, three
possible motions can be performed, i.e., straight motion,
locally self-rotation, and curvilinear rotation. Robots are
therefore able to find their master and to adjust their
speed and orientation for following. Once the master has
been identified, the system automatically changes to the
following mode by reducing the scanning range for
tracking the master and for increasing the system
bandwidth.
Fig. 4 Brief flowchart of the following algorithm.
External control and group autonomous movement:
A well-established communication network is the key
for successful manipulation of robot groups. This can be
further divided into two categories. The first category
concerns the interaction between the host and individual
robots and the established X-Bee protocol allows the host
computer send signal in a broadcasting manner to all
robots. These robots then decode the data to extract the
motion commands for their own and subsequently to
drive the servomotors to achieve the goal. By this
approach and careful motion planning, an external hosted
group motion can be achieved. On the other hand, by
mimicking the animal group motion behavior, the second
category deals with group autonomously movement. In
this task, one robot is assigned as the master and the rest
are slaves. The master received the command from the
host computer or by its own decision to plan the motion
based on sensed information. On the other hand, those
slave robots then move with the master robot to form
specific motion patterns. By this approach, an
autonomously guided robot group motion can be realized.
Wiimote indoor localization:
In order to monitor the location of each mobile robot in
real time for evaluating the effectiveness of our
algorithms, the multi-zone wiimote localization scheme
previously developed by us [5] is adapted here. As shown
in Figure 5, two wiimotes are mounted on the ceiling (2.3
m above) to cover an effective area of 1.82.7 m2 with a spatial resolution of 1.75mm for monitoring the motion.
In addition to this original purpose, it is immediately
realized that by properly utilizing the obtained global
position information, it is possible to perform more
sophisticated applications such as precision positioning of
an entire robot group and interactions between two
independent groups of autonomously robot in the future.
Fig. 5 The setup of the wiimote localization system
4. EXPERIMENTAL RESULTS Obstacle avoidance:
As shown in Figure 6a, a maze-like passage is
designed to test the performance of the obstacle
avoidance. A robot is commanded to walk through the
passage within 10 seconds without collisions with the
walls. On the other hand, a path with a dead end is also
used and the results shown in Figure 6b indicate that the
robot can successfully walk out from it without the
concern of trapping inside. These features are important
for indoor service robot since passages could be
complicated and with many dead ends exist inside a
typical living space. Robots without the above mentioned
abilities would be impractical for indoor service
applications.
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(a)
(b)
Fig. 6 Obstacle avoidance demonstrations
Collision avoidance between two robots:
In a multi-robot workspace, collisions between two or
more robots must be prohibited and this issue is
investigated here. This can be treated as a dynamic
situation of the above obstacle avoidance problem. As
shown in Figure 7a, two mobile robots approach each
other. Once the relative distance is within the threshold,
both robots will make decisions to avoid collisions based
on the obstacle avoidance algorithm addressed above.
Following:
Following between a group of slave robots and a
master robot is a fundamental technical ability for
subsequent realization in autonomous motion control of
group robots. Figure 7b demonstrates the results of our
algorithm in robot following. The master robot performs a
general planar motion and followed by a slave robot. As
one can see, the slave robot follows the path of the master
robot very well.
(a)
(b)
Fig. 7 Interactions between two robots (a) collision
avoidance and (b) following motion
External group control:
In this experiment, the host broadcasts a massage to all
three mobile robots and asks them moves toward their
corresponding destinations. The results are successfully
shown in Figure 8 and the result indicates that it is
possible to simultaneously control a large number of
robots for real applications (such as smart building
elements) by just sending an information package while
the bandwidth can still be maintained. This would be very
useful for smart building applications where a large
number of building element must be moved to specific
locations for fulfilling the functional requirements.
Fig. 8 An external group control demonstration
Autonomously movement:
Finally, autonomously movement experiments are
performed based on the above established technical
capabilities. Four our mobile robots are designed to
perform three tasks. First, we allow these four robots
moves freely with collision free in the workspace as
shown in Figure 9a. Next, as shown in Figure 9b, with
one robot serves as the master, all other slave robots
moves toward the master once they are asked. Finally,
these slave robots follow the master to form a following
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motion without collisions as shown in Figure 9c. The
successful demonstrations shown allow us to investigate
more realistic situation for realizing the real group robot
applications for indoor service in the future.
(a)
(b)
(c)
Fig. 9 Autonomously movement demonstration
5. SUMMARYAND CONCLUSION Autonomous control of a group of mobile robots has
the fundamental importance in bio-mimic or smart living
space related applications. However, several fundamental
concerns in communication, coordination, obstacle
avoidance, and cooperation must be solved and
demonstrated before detail task planning. In this work,
both hardware design and software algorithm
implementation are developed for establishing the
associated fundamental capability. In particular, this work
proposes a novel obstacle avoidance architecture and its
associated search algorithm to dynamically search
possible obstacles and update the moving trajectory in
real time. In addition, by integrating this scheme with the
global position information provided by the wiimote
multi-zone localization, it is expected more sophisticated
schemes such as the interaction between two
autonomously controlled robot groups can be further
developed in the future for indoor service applications..
ACKNOWLEDGEMENT This work is supported by National Science Council
under contact numbers: NSC97-2221-E-006-152-MY3
and NSC 99-2221-E-006-173-MY2.
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