Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems
Raleigh, NC July 7-10, 1992
Coordinating Mobile Robots by Applying Traffic Rules
Shin Kato , Sakae Nishiyama , Jun’ichi Takeno
School of Science and Technology , Meiji University.
1-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa 214 JAPAN
Fax +81-44-934-7912
Telephone +81-44-934-7454
Abstract--This p a p e r proposes a system to c o n t r o l
mobile r o b o t s by a p p l y i n g traffic rules. The authors
have constructed traffic rules to achieve safe a n d
s m o o t h m o v e m e n t of r o b o t s by collectively c o n s i d e r i n g i n f o r m a t i o n o n t h e w o r k e n v i r o n m e n t s of m o bile o b j e c t s s u c h a s m o b i l e r o b o t s a n d persons. T h e
traffic rules enable respective mobile robots using
self-control t o j u d g e t h e i r m o v e m e n t .
T h e system applies the traffic rules to m o b i l e
r o b o t s in o r d e r t o c o o r d i n a t e a c e r t a i n r a n g e o f
m o v e m e n t t h r o u g h m a x i m u m use of i n f o r m a t i o n o n
t h e e n v i r o n m e n t s of mobile r o b o t s s u c h a s t h e i r p a s sages, quantity, performances, a n d which a r e usually
a r e known.
Applying traffic rules to r o b o t m o v e m e n t in o r d e r
to c o o r d i n a t e r o b o t s is a n e w p r o p o s a l , e n a b l i n g t h e
realization of a system t h a t needs n o direct c o m m u n i cation such as a c o m m u n i c a t i o n s system.
T h i s p a p e r first outlines a traffic rules a p p l i c a t i o n
s y s t e m f o r m o b i l e r o b o t s , a n d e x p l a i n s h o w to c o n Also, t h e
s t r u c t t h e s y s t e m b y s h o w i n g examples.
p a p e r discusses t h e effects of t h e system, a n d how t o
a p p l y t h e t r a f f i c r u l e s t o a c t u a l systems.
Finally,
t h e p a p e r e x p l a i n s t h e e f f e c t i v e n e s s of t h e s y s t e m
that applies the traffic rules, a n d the attainment of
mobile r o b o t s using t h e system t h r o u g h e x p e r i m e n t s
with mobile r o b o t s m o u n t i n g u l t r a s o n i c sensors.
I. INTRODUCTION
With the progress in intelligent mobile robots, requests
have been made for robots that can efficiently move as desired
[ 1],[2],[3]. However, moving
mobile objects pose
numerous problems. First is avoiding collision, followed by
deadlock and paralysis due to congestion. T o solve these
problems, we had to consider the coordination of mobile
robots.
Studies of coordination of robots made so far have
concerned the use of communication systems 121 and local
collision avoidance of mobile robots 141. However, the
authors have proposed a method of coordinating mobile
robots by applying traffic rules to intelligent mobile robots
151,[61,[71,~31,
[919110 I.
Why must we apply traffic rules to robots? The reasons
are as follows:
To keep order in a society or group, common rules are
required.
With such rules, we can maintain order in
individuals having self-controlled will. Also, such rules will
help avoid, without direct communication, the confronting of
problems which could be solved by such rules.
Thus, we anticipated that mobile robots would be able to
move safely and smoothly if they are provided with common
rules (traffic rules) for movement. Our system is to apply
such traffic rules to mobile robots.
The major objective of this study was to construct traffic
rules to be the knowledge of intelligent mobile robots by
fully using information, obtained in advance, on work
environments and mobile objects., to propose a more
sophisticated intelligent robot system such as coordination
among mobile robols, and to discuss the possibilities of
realizing the system.
A . What are the traffic rules?
First, let us define traffic rules for mobile robots as ”Rules
that impose a certain level of order on mobile objects such as
mobile robots and persons, and work environments.”
The construction of traffic rules tnust be determined as
optimally as possible by combining the information on work
environments of mobile robots, performance of mobile
objects, and the quantity of mobile robots. We intend to
have moving objects such as mobile robots and persons in a
work environment follow the rules, thereby achieving their
smooth movement by avoiding collision (Fig.1 , Fig.2).
Applying traffic rules provides all mobile objects in the
related environment with common rules on movement,
ensuring coordinated movement among mobile objects to
avoid collision. Also, the rules supplement information on
judging movement, thereby making the activities within the
Information on work
environment
Information on mobile objects,
e.g., mobile robots and persons
Customer
& minimum construction.
. This rules vary depending on the
performances of the mobile objects.
+U
Mobile obiects of various tvpes
Fig. 1 Information required for constructing traffic rules
0-7803-0737-2/92$03.0019920IEEE
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111. CONSTRUCTINGTRAFFICRULES
A. Elements of traffic rules
In an intelligent mobile robot system, the information we
can obtain in advance is that on mobile objects and
environments.
The information on mobile objects is that on the
performance of mobile robots (measuring range of sensors,
measured speeds, stopping accuracy, running speed,
minimum turning radius, reliability, and controllability), the
size, shape, quantity, type, of robots, and existence of
persons. The information on environments is the width of
passages, shape and number of intersections. Also, the
information that mobile robots require when moving or the
following information, constitutes the elements of traffic rule
construction.
l-7
Work roo
-
\
With respect to the information on the measuring range
of sensors, at a minimum information is required on the
position of the next destination (whether an obstacle
exists), and on the positions through which another
mobile object could pass to reach the destination
(whether an obstacle exists).
* Global map information (routes to the destination).
Information on the present position.
Fig.2 An example of intelligent mobile robots
to which traffic rules are applied
entire environment safe and smooth. Thus, the rules would
be capable of efficiently operating mobile robots that can
cope with congestion and deadlock.
The authors have thus considered the environment
collectively by using known information, and propose, as a
system to coordinate mobile robots, a system to apply traffic
rules to mobile robots which judge movement using 'selfcontrol.
The authors call the system for applying traffic rules to
mobile robots "Traffic Rule Application System (TRAS)
[ 101."
Conditions requested bom customers concerning intelligent
mobile robots must also be considered. Thus, we must
construct traffic rules by considering these elements so that
robots meet such requirements to the extent possible.
B. Classibing traffic rules
Traffic rules can be classified roughly into three as follows:
B. What are traffic rules?
@Traffic rules to be applied to the current positions of
mobile robots.
This is to judge robot behavior by collating the current
positions of mobile robots with the global map.
(example: passage zone, stop, slow)
In an environment where mobile robots move about,
various problems may occur.
Collision avoidance
Congestion
Deadlock
Traffic rules might be a remedy for the above conditions
which could hinder the safety and smooth movement of
mobile objects within an environment.
First, by simplifying the question, we can define
"collision" as "simultaneous existence of another mobile
object at the same position in the route of a mobile object."
Also, we can define "congestion" as "a condition in which
the movement of other mobile objects prevent a mobile
object from reaching its destination within the required time
at the required speed." "Deadlock" can be defined as
"movement activities which become an endless loop or
which stop completely during movement of mobile objects."
Collision causes deadlock.
@Traffic rules to be applied to current positions and
conditions.
This is used by robots to judge their behavior in the next
step when they have detected mobile objects using a
sensor.
Example: Overtaking, avoiding obstacles, how to cross
intersections.
0 Traffic rules to ensure safety for exceptional processing
This is to ensure safety in case of an accident such as
failure.
C . Exumples for constructing traffic rules
Procedures for constructing traffic rules are as follows:
@ T o ensure safe and smooth movement, reduce wherever
possible the places where robots could collide from the
entire environment.
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@For the other places where robots could collide, establish
traffic rules containing preference by considering safe and
smooth movement.
Here, let us show collision only.
As explained above, collision can be said to occur by the
simultaneous occupation of one route by plural objects.
Thus, we can avoid every collision by taking suitable
measures for all the intersections in the related environment.
Let us enumerate the overlapped routes: routes in the
opposite directions and same directions, intersections. We
can avoid collision by constructing traffic rules that cope
with all of these overlapped routes. To decrease places where
collision could occur, providing passage zones for each
direction are effective. Restricting the directions of
movement is advantageous for robots because it allows them
to anticipate the movement of the other mobile objects to
some extent.
Also, we can limit the collision-occurring places to
intersections only if we inhibit overtaking. Thus,
establishing traffic rules at intersections would be enough to
avoid collision (Table I).
Preconditions: No passing, all robots movc at constant speed, routes of
constant width.
How to apply the rules to actual mobile robots.
Stops the robot so tha.t the distance
information from the preceding sensor is kept
above a specific value.
Controls the robot so that thc distance
Keeping
- - sufficient
side space.
1 information from the side sensors is kcpt
' above a specific value.
Passage zone
Instructs the robot to movc on the right side
(keeping right, etc.) in the global map by detecting the position of
the robot.
' * Has the sensor detect the right side of the
route using the wall, and moves the robot to
the right.
How to cross an
' Confirms with a sensor whether a mobile
intersection
object exists on thc right sidc, and instructs
Preference to right ' the robot to stop if a robot exists.
turn.
The robot judges where it is on the global
Preference toward a map by detecting its position, and checks at a
right side mobile
pre-detcrmined position by sensor whether an
object.
obstacle exists in an area to be searched.
Collision
* Obstacle exists:
Stops until it is removed.
avoidance.
No obstaclc exists: Advances.
Uses a communication system.
Avoiding deadlock
Avoids collision by following the traffic
Prefcrcnce at
rules on crossing an intersection.
inlcrsections
Stops with sufficient in-between distance to
thc preceding object by keeping ample space
Obstacles, e.g.,
failure
ahead. Sets the maximum waiting time.
Keeping sufficicnt
space in front.
1
'
Apter the
set time
A . Purposes of and conditions for the movement of mobile
robots.
The main purpose of mobile robots is to move to different
places. The conditions that mobile robots require when
moving are as follows:
Destination
Conditions on time
(time required, time restrictions, time designations,
allocation of time)
Conditions on distance (travelling distance)
Conditions on energy consumpition
Conditions of safety
Conditions on achieving purposes
e Conditions on relative work importance
B . Optimizing the requests from customers
C. Optimizing individual mobile> robots and the whole
system
-
pa-ses, closcs the rout=, ~ n d
returns to the intersection through which it
moved to the closed route. 'Then, re-constructs
AND SYSTEM OPTIMKATION
An intelligent mobile robot system must be able to move
robots optimally while meeting the requests from customers.
Requests from customers are diversified as noted above. We
consider that robots usually must satisfy two or more of
them, not just one. Also, quite a few conditions affect the
others. Thus, we must consider the order of preference for
the conditions.
Further, it should be clarified if the optimization of
conditions can be actually evaluated when the routes are being
planned. In a system for mobile robots, it is difficult to
evaluate when planning the routes whether the conditions on
mobile robots are optimally established if the system does
not allow control of the routes of all robots from a centralized
operator panel. Thus, in a system for self-controlled mobile
robots, it is difficult to evaluate when planning the routes
whether all robots are optimally designed. And, the
optimality of individual robots often changes after the routes
have been planned, because the optimality of mobile robots
is affected by the movement of other robots. Thus,
optimality cannot be finally evaluated until a movement is
completed.
In a self-controlled mobile robot system, it is desirable to
plan an optimum movement so that individual robots meet
given conditions, and to re-plan the optimum movement each
time the plan is changed.
TABLE I
AN EXAMPLE OF COXSIRCC?I~XG TKAITIC
RUI-I~S
Traffic rules
Iv. REQUESTSFROM CUSTOMERS
If no robot interferes with the other robots in a system for
mobile robots, optimizing indiividual mobile robots
optimizes the entire system. Thus, in optimizing an entire
system, optimizing individual mobile robots is an important
element. However, optimizing individual robots is
insufficient for moving robots because the optimality could
change due to the problem of collision avoidance if robots are
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in fact affected by the other robots. Also, to optimize the
entire system concerning safety, non-collision routes of all
mobile objects should be provided. However, the
optimization of an entire system is effective only in
environments that contain no indefinite factors.
As explained above, the optimization of the conditions on
individual mobile robots and the whole system cannot be
studied separately. Concerning the system for mobile robots,
rules should be established to coordinate the optimization of
the conditions on individual mobile objects and the safe and
smooth movement of all objects. We believe that it is our
traffic rules that play this role.
D . Optimization of the system to which traffic rules are
applied
The probability of collision (CP) is inversely proportional
to the area of the routes, and directly proportional to the
number of mobile objects. Also, the probability of collision
shows that the number of mobile objects is limited in certain
environments (Fig. 3). The smaller the probability, the
smoother the movement.
Next, let us show how the probability of collision changes
by restricting the moving range of mobile objects by setting
passage zones at places where collision could occur.
First, let us assume that collision could occur at
intersections only. Thus, the probability of collision in this
case can be obtained by multiplying the total probability of
collision by the ratio of the total area of the route and the area
of the places where collision could occur.
CP’ = CP C a P a
In a system to which traffic rules are applied, robots move
along the routes with reduced chance of collision according to
the optimized conditions of individual mobile robots.
Robots will judge their movement according to the traffic
rules if they are affected by the movement of other mobile
objects.
These traffic rules are the best method of ensuring the safe
and smooth movement of robots, and are standard for
optimizing an entire system. That is, in a system to which
traffic rules are applied, the entire system is optimized by
judging the movement according to the traffic rules with
optimized individual mobile objects.
V. EFFECTSOF APPLYING
TRAFFIC
RULES,
AND HOW TO APPLY THEM TO ROBOTS
A , Decrease in probability of collision
Let us consider the probability of collision in an
environment in order to verify the effect of using traffic rules
to coordinate mobile robots. It is very difficult to obtain the
precise probability of collision at one location in an
environment through calculation. However, we can obtain the
rough probability of collision by simply calculating the
combination of conditions that prevent the collision of
mobile objects using the areas of the routes and the mobile
objects.
The probability of collision (CP) can be expressed by the
following equation.
Thus, the probability of collision would greatly decrease,
enabling the use of more mobile objects.
Decreasing the places where collision could occur also
decreases collision-avoiding activities. Thus, it is understood
that, to coordinate mobile objects, decreasing the probability
of collision itself is very important.
B . Decreasing the detecting runge of the sensor
Applying traffic rules restricts the movement of mobile
objects, but restrictions enable mobile robots to predict the
movement of other mobile objects to some extent, allowing
us to limit the detecting range of the sensor according to the
information on mobile robots.
Thus, a mobile robot does not need to search all of the
areas around it with a sensor using the traffic rules possessed
as common knowledge. It needs only to search the areas
where other mobile objects could appear. This is called
1.0
-
0.8
-
2 0.6
-
8
s
.-
-s
i
h
CP = 1 - (X! / (X” (X - n)!)) = 1 - xCn
Total area of intersections Ca: 10
2
0.4
-
/
D
E
where, n < X,
xCn: combination
X = Pa / Oa: The maximum number of mobile
objects in the route (to be handled as
an integer in calculation)
where, CP: Probability of collision
Pa : Total area of route
Oa : Area per mobile object
n : Number of mobile objects
Ca : Total area of intersections
a
0
When intersections are assumed to be
the only places where collision can occur.
5
10
15
Number of mobile object n
Fig.? Probability of collision
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20
"limiting the sensor detecting range." Limiting the sensor
detecting range has enabled the speeding up of processing, and
handling of the interference with active sensors.
C. Solving the deadlock problem
To solve the deadlock problem, various methods have been
considered 151,~6],[71,[~],[101.
Of the methods, the method of
avoiding collision to prevent deadlock using the sensor
detecting range is promising, and has already been proven
effective on actual systems 16],[7],[8],11Oj.
VI. EXPEBIMENT
WITH ACTUALSYSTEMS
A. Outline of the experiment
The authors have conducted an experiment on applying the
traffic rules using actual intelligent robots, in order to
determine whether our proposal can be realized using an
actual system.
B. Constructing the truffic rules
All mobile robots in the same environment are to be
subjected to the same traffic rules. Simple traffic rules
shown below are applied.
Keep right (passage zone designated) --- A robot controls
the passage and the direction of its movement by
detecting its own position.
Safety space ahead --- Maintains a space of a certain
distance or more behind a mobile object ahead.
No-passing --- No route change made to avoid collision.
Collision avoidance (securing safety) --- Detecting a
mobile object within a sensor-detecting range, a robot
stops until the object moves out of range.
Persons must watch front, right, and left carefully, and are
allowed to move if no robot is approaching, and must stop if
a robot is approaching.
Considering the capacity of a robot, persons walk at an
almost constant speed of approx. 3 km per hour in this
experiment.
To avoid collision, a robot will stop immediately when
collision is anticipated through judgment of the possibility of
collision, without proceeding to the next action. Generally,
this method alone can avoid deadlock unless an obstacle
causing a complete stopping exists due to a failure. Also,
when the sensor -detecting range is correctly set, no deadlock
will be reached in which both objelcts stop simultaneously
due to collision [lo].
D . Equipment for experiment
The equipment used for the expieriment comprises two
intelligent mobile robots, and the ultrasonic sensors mounted
on the robots. The robots, HOMEROS and CAN-Boy,
completely having self-controlled intelligence, were prepared
by modifying the HER02000 robots(Heath CO.),mounting
visual sensing systems and computers for control.
The external sensors were the ultrasonic sensors mounted
on the HER02000. Mounted on the top center of the robots,
the sensors were able to rotate 360 degrees (1 second per
rotation) and measure 0 to 127.5 inches in 24 directions every
15 degrees. Also, they were designed by software to be able
to measure in any desired direction .
The robots continuously moved ai. approx. 30 cm or less
per second with the sensors working without interruption.
The robots moved approx. 5 cm before they stopped after
receiving a stop command. They turned to the right and left
by 90 degrees (rotation at polar center).
E. Controlling the sensor-detecting range
In the experiment, the detecting r,ange of the sensor was
automatically changed according to the position of the robot.
The detecting range of the sensor was calculated according to
the information on the environment ,and the traffic rules 7),
8), lo). The detecting range of the sensor covered the range
where no obstacle was allowed to exist so that the robot was
able to move the next time. If an obstacle was detected in the
detecting range, the robot was stopped immediately to ensure
safety .
+
Direction of
movement
C. Detecting obstacles und avoiding collision
To detect obstacles, the external sensors observe the
sensor-detecting range as determined by the provided global
map and the current position of the mobile robot.
Whether a robot will collide is judged merely by
determining whether an obstacle will hinder the movement of
the robot, or whether an obstacle exists within the pre-set
sensor-detecting range, not by predicting the movement of an
obstacle through the continuous measurement of its position.
Thus, the processing time for collision judgment has been
shortened to almost the equivalent of the detecting time of the
sensor.
SONAR-S?R
IF the distance to LLP, LKP plus KOBOTK is
SONAR-STR is satisfied,
I KOBOTR
V
'
'
I
I
the measured distance from the current robot
position in obtained by:
Distance to LLP, LKP plus KOBOTR
where, SONAR-!S.TR is
measuring range of
the sensor determined when the robot advances
straight.
Fig. 4 Sensor detecting range at L-shaped route
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-
The actual sensor was designed to measure the hatched
sectorial range in Fig. 4, which shows in example of the
detecting range of the sensor in an L-shaped route.
870(cm)
4
I20
I
F . Experimental method
Through the experiment, we prepared a control program for
mobile robots.
These were simple models of actual
intelligent robots. The program covered the application of
the traffic rules, processing of movement route planning, and
global maps.
Two completely self-controlled mobile robots, controlled
by a mobile robot control program based on the traffic rules,
traveled the route smoothly.
0
~
@ : Graphed nodal point No.
Details of the experiment method are as follows:
First, the robots were provided with global maps of the
entire environment. Then, the operator gave the robots
information on the starting position and destination.
The robots worked as follows:
@Planned the shortest route from the starting position to
the destination.
@Extracted the local maps from the global map for route
and the intersections in the order of movement.
@Moved along the planned route.
@Determined the sensor-detecting range according to the
traffic rules by refemng to their positions and local map.
@Observed the work space using the external sensors.
@Detected obstacles through the results of the observation.
0Judged, according to the traffic rules, whether they would
collide with the other objects.
@Judged what to do according to traffic rules.
@Moved, or stopped.
@Returned to Q .
Fig. 5 Dimensional drawing of work environment
The route graphs used for the experiment were nondirectional (Fig.G), except for the connections at contacts,
which were directional to enable one-way traffic. In the
experiment, however, no one-way route was used.
The information on the coordinates, shapes, and connections of nodal points were contained in the program in the
form shown in Table 11. Where, rd[O][4] = ON: the
connection from the nodal point 0 to 4. S : a straight route,
or end point. L: L-shaped route. T: T-shaped route. X: a
cross-route. The coordinates of nodal points were the centers
of the routes of each shape.
The experiment was carried out on the following routes.
The robot R I moved from the starting point 0 to the nodal
point 6 (destination)(O -+ 4 -+ 5 46). The robot R2 moved
from the nodal point 2 to the nodal point 8 (destination) (2
+ 1 -+ 5 -+ 8).
-
By repeatedly following steps 0 @
the robots
I, moved
to the destination along the planned shortest route.
The shortest route was obtained by the A*algorithm with
the distances between nodal points as cost.
Each local map to be extracted from the global map
covered a route from one nodal point to the next nodal point.
The robots confirmed the connection to the subsequent nodal
point for judging whether to turn to the right or left, or to
advance straight ahead. Thus, the robots automatically
selected the traffic rules according to the conditions.
The robots were able to move along the shortest route
unless affected by other mobile robots. If affected, the robots
secured safety using the traffic rules, and the entire system
was optimized.
G. Global mup und experiment route
The experimental environment inside the room was
partitioned with furniture such as desks, that is, a simple
work environment (Fig.5).
Fig.6 Graphed global map (10 nodal points)
TABLE D
T i i o ~ n n o ox
x TIE C o i x ~ c n o ~ sTHE
o r COORDNATZS
AMI SHAFTS
OF NODALFONE
Nodal points Coordinates Shape Connections of nodal points
0
(60,420)
S
rd 101 [4] =ON
1
(0,420)
L
rd [ l ] [2] = rd 111 [5] = ON
2
(630,420)
L
rd 121 111 =rd 121 [6] = O N
3
(810,420)
S
rd 131 [ Y l = O N
4
(60,240)
T
rd [41 LO] = rd [41 [SI = rd [41 [71= ON
5
(330,240)
X
rd [SI [ l ] = rd 151 [41 = rd [SI 161
= rd 151 [SI = ON
6
(630,240)
L
rd 161 121 = rd [6] [ 5 ] = ON
7
(60,601
L
rd 171 [4] = rd 171 [8]= ON
T
rd [SI [SI = rd [8] [ 7 ]= rd [8] [91 = ON
(330.60)
8
9
(810,60)
L
rd 191 131 = rd 191 [81 = ON
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H . Results of the experiment
This system is effective in the following cases:
At the cross-route of nodal point 5, robot R1 stopped to
avoid collision and moved smoothly.
Fig.7 shows the state of the experiment.
When mobile robots increase or decrease in an
environment where mobile robots exist.
When efficient movement is required by coordinating
different or plural mobile robots.
* In an environment where indefinite factors are contained
such as failures, joint work with persons, and operators.
When moving robots under self-control.
When constructing a system of wide freedom.
I . Consideration ojthe experimentul results
The experiment has shown that a system for intelligent
mobile robots can be achieved by applying traffic rules,
including the processing of movement route planning and
global maps. Also, experiments on an actual system have
demonstrated that self-controlled mobile robots can move
without colliding under the same traffic rules without using
any communication means.
This experiment was conducted in a space not partitioned
with walls. We intend to conduct a similar experiment in an
actual work environment. We understand that one of our tasks
is to realize a system for intelligent mobile robots by
applying traffic rules including the solution of deadlock due
to failure.
Also, ultrasonic sensors could cause problems such as
irregular reflection depending on the shapes of walls and
pillars. Thus, there is risk to relying only on ultrasonic
sensors for external sensing. We should use them together
with other types of sensors. Detecting the positions of a
robot with built-in sensors is insufficient when the robot
moves a long distance.
Thus, it is necessary to control the stance and correct the
position of robots in reference to the walls and pillars in
actual environments. These are our tasks to be solved in the
future.
vu. CONCLUSION
We have proposed jn this paper a system to control mobile
robots by applying traffic rules that have been constructed to
achieve safe and smooth movement of robots by collectively
considering information on the work environments of mobile
objects such as mobile robots and persons. The traffic rules
enable respective mobile robots to judge their movement
under self-control.
We have proposed our plans for the actual applications of
this system in Autonomous Mobile Robots, Cars, and
Construction Material Transportation Systems for UltraHigh-Rise Buildings [SI,[ 101.
The experiments have shown that the proposed system of
applying traffic rules to mobile robots is highly effective, and
can be realized as a system for intelligent mobile robots.
With respect to the coordination of mobile robots,
applying traffic rules to mobile rlobots is an entirely new
proposal leading to the realization of systems that require no
direct communication such as a communication system.
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Fig.7 Experiment status
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