COOPERATIVE MAP BUILDING USING QUALITATIVE

REASONING FOR SEVERAL AIBO ROBOTS

David Graullera, Salvador Moreno

Instituto de Robótica,Universidad de Valencia, Paterna, Valencia, Spain.

M. Teresa Escrig

Departamento de Ingeniería y Ciencia de los Computadores, Universitat Jaume I, Castellón, Spain.

Keywords: Qualitative Reasoning, Cooperative Map Building, Multi-Robot System.

Abstract: The problem that a robot navigates autonomously through its environment, builds its own map and localizes

itself in the map, is still an open problem. It is known as the SLAM (Simultaneous Localization and Map

Building) problem. This problem is made even more difficult when we have several robots cooperating to

build a common map of an unknown environment, due to the problem of map integration of several

submaps built independently by each robot, and with a high degree of error, making the map matching

specially difficult. Most of the approaches to solve map building problems are quantitative, resulting in a

great computational cost and a low level of abstraction. In order to fulfil these drawbacks qualitative models

have been recently used. However, qualitative models are non deterministic. Therefore, the solution recently

adopted has been to mix both qualitative and quantitative models to represent the environment and build

maps. However, no reasoning process has been used to deal with the information stored in maps up to now,

therefore maps are only static storage of landmarks. In this paper we propose a novel method for

cooperative map building based on hybrid (qualitative+quantitative) representation which includes also a

reasoning process. Distinctive landmarks acquisition for map representation is provided by the cognitive

vision and infrared modules which compute differences from the expected data according to the current map

and the actual information perceived. We will store in the map the relative orientation information of the

landmarks which appear in the environment, after a qualitative reasoning process, therefore the map will be

independent of the point of view of the robot. Map integration will then be achieved by localizing each

robot in the maps made by the other robots, through a process of pattern matching of the hybrid maps

elaborated by each robot, resulting in an integrated map which all robots share, and which is the main

objective of this work. This map building method is currently being tested on a team of Sony AIBO four

legged robots.

1 INTRODUCTION

An autonomous mobile robot, able to explore an

unknown but structured environment, must first be

able to perform several related tasks, which can be

illustrated by the answers to the following questions

(Levitt, 1990):

• What should I remember? (mapping)

• Where am I? (localization)

• Where should I go? (path planning)

• How can I go? (motion control or navigation)

Acquiring and maintaining internal maps of the

world is a necessary task to carry out a successful

navigation in complex environments.

We are going to solve in this paper the problem

of map building for several cooperating autonomous

mobile robots on an unknown labyrinth made of

rectangular walls. The walls are distributed

randomly forming a labyrinth and the robots are left

inside with no knowledge of the environment. The

robots have to explore the environment and to

cooperate to build a map of the environment.

There are in the literature a lot of approaches for

building maps in static, structured and relatively

229

Graullera D., Moreno S. and Escrig M. (2006).

COOPERATIVE MAP BUILDING USING QUALITATIVE REASONING FOR SEVERAL AIBO ROBOTS.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 229-234

DOI: 10.5220/0001218502290234

 SciTePress

small environments. They can be divided into three

main strategies: qualitative, quantitative and hybrid

approaches.

Qualitative models focus on the boundaries of

the objects, making divisions of the space more or

less detailed. These approaches deal with imprecise

information in a manner inspired by the cognitive

processes used by humans. The qualitative concept

of a topological map, which represents the world

using nodes (places) and arcs (relations), has been

used in several approaches, such as the one

introduced by (Kuipers, 1978). Another model is

defined by (Freksa, 2000), where schematic maps

are used to reason about relative positions and

orientations. Other qualitative models have been

carried out by several authors. Most of these

qualitative models have been implemented mainly in

simulations.

Quantitative methods represent the environment

by metrical information obtained by the sensors. The

major exponent of this strategy is the grid-based

model, introduced by (Moravec and Elfes, 1985).

Quantitative models are affected by odometric and

sensory errors. In recent years, many quantitative

approaches have been developed using probabilistic

techniques to cope with partial and inaccurate

information. All of these approaches are based in

implementations of the Bayes filter, as the Kalman

filter, hidden Markov models, partially observable

Markov decision processes or Monte Carlo

localization. A survey on this techniques can be

found in (Thrun, 2001) and (Thrun, 2002).

Hybrid approaches handle with qualitative and

quantitative information, combining the best of the

each model. One of the first models for map

building was proposed by (Thrun, 1998), which

combines the occupancy grids with topological

maps. Other hybrid models can be found in

numerous papers, as (Escrig, 2005). More hybrid

models use probabilistic techniques to cope with

partial information.

The work presented in this paper represents

hybrid information into a map: quantitative data

provided by robot sensors (most of the times this

data contain imprecision); and qualitative data.

Moreover, our approach is going to use a qualitative

reasoning process which will allow us to solve the

four problems above mentioned: mapping,

localization, planning and navigation.

2 COOPERATIVE MAP

BUILDING

We are going to solve the problem of cooperative

map building for several autonomous mobile robots

on an unknown labyrinth made of rectangular walls.

We suppose each robot is able to explore an area in

front of it, in order to detect if this area is free or if it

is occupied by one or more walls, or by other robot.

Taking into account that the walls are straight, each

robot must detect the distance and orientation of

each wall which enters in its exploring area, and the

position of a circle enclosing each other robot it

detects in the area. In the case of AIBO robots, we

have implemented a multisensory approach to

explore this area, by using the TV camera of the

robot, and the infrared range sensor.

The cooperative map building procedure is

composed of the following steps:

• Individual Map Building

• Map Sharing and Self-Localization

• Integrated Map Building

3 INDIVIDUAL MAP BUILDING

For each robot, we build a individual map taking as

input the distance and orientation of each wall which

enters in its exploring area, and the position of a

circle enclosing each other robot it detects in the

area, and have as output the generated map, and the

orders to the robot for exploring the environment, in

the terms of “walk XX centimetres”, and “turn YY

degrees”.

3.1 Initialization

At the beginning, the robot map is empty, and the

robot detects nothing in the area in front of it,

therefore it assumes the initial hypothesis that the

scenario is composed of an infinite floor surface,

without walls, with only an explored point: the

current position of the robot. The robot starts

walking ahead until the explored area in front of the

robot is occupied by a wall. This situation can be

seen at the snapshot 1 of figure 1. Each step the

robot gives is memorized in the database (only to

make an approximate evaluation of the distance the

robot walks in free walking). When the robot detects

a wall, it enters into the wall following mode.

ICINCO 2006 - ROBOTICS AND AUTOMATION

230

3.2 Wall Following

When the robot detects an unknown wall, it starts

following the wall by turning right and following the

wall. While it is walking along a straight wall, it

stores the distance it walks as an approximate

measure of the length of the wall. When it reaches a

corner, it labels the corner and it stores the

approximate turning angle of the corner before start

following a new wall. This process can be seen at

snapshots 2, 3 and 4 of figure 1.

Nevertheless, the process is not as simple as

this. The imprecise information about robot position

and orientation given by odometry, makes

impossible to generate a map of the scenario as the

one shown in figure 1. In fact, the real map is more

or less as the one shown in figure 2.

The map information stored in the database

when the robot discovers point d is the following:

point(nonreal,p01).

point(real,a,95).

line(p01,a,3).

point(real,b,100).

line(a,b,3).

point(real,c,105).

line(b,c,1).

point(real,d,-85).

line(c,d,1).

Taking into account the uncertainty in direction

and length, we can easily see that it will be

impossible to recognize directly the corner i as the

corner a, therefore it will be seen as a different one

and the robot will give infinite loops around the

room. This process will end when the following

module, the hybrid shape pattern matching, proposes

that corner a is corner i, so the shape is totally

explored.

3 4

Figure 1: An example of scenario.

Figure 2: The scenario map as build by the wall following process alone.

COOPERATIVE MAP BUILDING USING QUALITATIVE REASONING FOR SEVERAL AIBO ROBOTS

231

3.3 Hybrid Shape Pattern Matching

The hybrid shape pattern matching is continuously

monitoring the output of the previous module, which

can be seen in figure 3. In this figure, we can see

from left to right the movements of the robot, as a

straight line for each straight step, an angle down for

inner corners, and an angle up for external corners.

The pattern matching process tries to detect cycles in

the trace shown in figure 3. The first hypothesis

which we can make is to suppose that corner a is the

same as corner i, but the pattern matching process is

only sure when corner d is revisited, as is one of the

two only external corners of the scenario, and it is

very difficult to misrecognize this.

Note that this hypothesis can be wrong,

therefore it will be stated and maintained only if

subsequent measures are compatible with this

hypothesis. If not, the hypothesis must be revised

through a truth maintenance process which we

implement thanks to the backtracking mechanism of

prolog, the language we have implemented this

algorithm.

Once the hypothesis has been stated, the

scenario map is corrected under the assumption that

corner a is corner i, and the position and orientation

error between i and a, is cancelled by splitting it in

several minor corrections to angles and distances in

order to achieve that point a and point i will be the

same, resulting in a map as the one seen in figure 4.

3.4 Inner/Outer Area Exploration

Once the shape of the scenario has been resolved, it

is necessary to see if it is a shape enclosing the

robot, or if it is a shape surrounded by the robot.

This can be seen by simply seeing if the total angle

is +360 degrees, in which case the robot is inside the

shape, or -360 degrees, if the robot has surrounded a

shape from the outside. Now it is necessary to split

all the unexplored area by using the two-dimensional

qualitative orientation model of (Zimmerman and

Freksa, 1993). The model defines a Reference

System (RS) formed by two points, a and b, which

establishes the left/right dichotomy. The fine RS

includes two perpendicular lines by the points a and

b. This RS divides the space into 15 qualitative

regions (Figure5a). An iconical representation of the

fine RS and the names of the regions are shown in

Figure 5b).

obstacle

cycle i=a

a b c d e f g

Figure 3: The hybrid pattern matching process.

b=j

a=i

d e

Figure 4: The scenario map corrected by the hypothesis a=i.

ICINCO 2006 - ROBOTICS AND AUTOMATION

232

lf sf rf

l idf r

lm sm rm

ibl ib ibr

bl sb br

a) b)

Figure 5: a) The fine RS and b) its iconical representation.

The information which can be represented by

this RS is the qualitative orientation of a point

object, c, with respect to (wrt) the RS formed by the

point objects a and b, that is, c wrt ab. The

relationship c wrt ab can also be expressed in other

five different ways: c wrt ba, a wrt bc, a wrt cb, b

wrt ac and b wrt ca, which are the result of applying

the inverse (INV), homing (HM), homing-inverse

(HMI), shortcut (SC), and shortcut-inverse (SCI)

operations, respectively.

The composition of relationships for this model,

what we call Basic Step of the Inference Process

(BSIP), on qualitative spatial orientation is defined

such that “given the relationships c wrt ab and d wrt

bc, we want to obtain the relationship d wrt ab”.

The idea is to split the unexplored area in

several subareas where the position of the robot does

not vary with respect to any pair of corners of the

scenario shape. As there are many subdivisions,

secondly, we select not all, but only a few, which

makes all the subareas convex, and which

maximizes the probability of recognizing the corner

(for instance, corners d and e will be preferent). In

our case, the whole area is splitted in three convex

regions: abcd, dahe, and fehg. Then the robot starts

walking along the borders of these regions (lines a-d

and e-h).

If the robot found a wall while exploring the

area, the shape is explored by using the same

procedure of wall following and hybrid pattern

matching. If not, the subareas are finally explored

until the map is complete, or until the cooperative

map building process decides that the integrated map

is complete and that the individual maps must not be

completed, as it would result in duplication of effort.

4 MAP SHARING AND

SELF-LOCALIZATION

Each robot builds its own map independently

according to the procedure stated in the previous

point. In this point, which is made concurrently with

the previous one, each robot shares its individual

map with the other robots, so each robot has a copy

of the maps of the other ones. The objective is that

each robot must self-localize itself in the maps of the

other robots.

The procedure is very similar to the problem of

SLAM (Simultaneous Localization and Map

Building problem), which is described in (Lu, 1997).

In the SLAM problem, a mobile robot must explore

an unknown environment and to build a map of the

environment. Once the map is built, the robot is

kidnapped and located in other place of the

labyrinth. The robot must then be able to localize

itself in its map. A solution for this problem, making

use of an hybrid approach very close to the one we

describe, is described in (Escrig, 2005).

The key problem of the SLAM, which is the

same of this point, is to localize the robot in a map

already built. Is not important if the robot must

localize itself in a map of the environment made by

itself, or made by other robot.

The hybrid map of the environment,

corresponding to the exploration of figure 1, will be

the following (from point a to e):

point(real,a,95).

point(real,b,100).

line(a,b,3).

point(real,c,105).

line(b,c,1).

rel(a,b,c,[r]).

point(real,d,-85).

line(c,d,1).

rel(b,c,d,[r]).

point(real,e,-90).

line(d,e,1).

rel(c,d,e,[l]).

Where the predicate rel(a,b,c,[r]) means that the

qualitative orientation c wrt ab is r (see figure 5).

The process of self-localization of the robot in

the maps of others robots takes into account the own

map of each robot, and the fact that the robot is

localized in this map. Then the map of each robot is

compared with the maps of other robots, first by

comparing the qualitative orientation of the corners

(In the above example, the rel/4 predicate, which

says to us that the corner c is located at the right [r]

of system ab, that corner d is located at the right [r]

of system bc, and so on). The comparison of the

qualitative orientations is very simple in

computational cost and gives a few possibilities of

matching, which are then compared quantitatively

by using the line/3 and the point/3 predicates, which

allow us to compare the angles and distances, and

then the better matching is selected.

COOPERATIVE MAP BUILDING USING QUALITATIVE REASONING FOR SEVERAL AIBO ROBOTS

233

A simplification of this process is when a robot

detects another one, because then it is possible to

take advantage of the fact that two maps must match

exactly starting from the localization of their robots,

thus making simpler this process.

5 INTEGRATED MAP BUILDING

The last point is to integrate all maps in a single

map. This process can be done starting from the

individual maps and the matching corners that have

been identified in the previous point. This allows to

assert as working hypothesis (which can be wrong,

so this point must take into account a possible

backtracking of matching points) an integrated map

and the matching of each individual map in the

cooperative map. This hypothesis is maintained until

a robot generates a individual map which does not

match with the integrated map, resulting in a

backtracking process to detect which corner

matching is erroneous, according to new data. This

is a constraint solving problem, in which we offer

the integrated map as the map which better adjusts to

each individual map, but taking into account that is

not the only solution (for example, a solution where

the environment is composed of all individual maps

without any matching is always possible, but is not

the simpler one). A good way to select the working

hypothesis is to select the integrated map with fewer

corners of all solutions, compatible with current

matchings.

Finally, the integrated map building process

must decide if the integrated map is complete. If it

is, it will send a signal to all robots to make them

interrupt its individual map building process and to

accept the integrated map as complete, and to give

the environment as completely explored. If we do

not take this step, each robot will individually

explore the environment, so the exploring work

would be repeated as many times as robots we have,

and the idea is to accelerate the environment

exploration by using more robots.

6 CONCLUSIONS

This paper describes a procedure to the problem of

exploring a unknown environment with several

robots, which makes the exploration faster as the

number of robots increases. This is a good procedure

for starting cooperative works with several mobile

robots, as for example to explore an area for finding

things, or for vacuum cleaning of huge surfaces (as

commercial centres), and so on. The algorithm can

be programmed in a main host connected by

wireless with the mobile robots, or can be

implemented in each robot without a central host

(useful for autonomous systems), if a common

memory is shared among them by wireless.

Currently we are working on its implementation on a

team of Sony AIBO four legged robots, on an

unknown environment composed of boxes on a

room, to form a labyrinth which must be explored.

AKNOWLEDGEMENTS

This work is partly supported by the Spanish CICYT

project TIC 2003-07182.

REFERENCES

Lu, F., Milios, E., 1997. Globally consistent range scan

alignment for environment mapping. In Autonomous

Robots, 4:333-349.

Levitt, T.S., Lawton, D.T., 1990. Qualitative Navigation

for Mobile Robots, In AI, Vol. 44, 305-360.

Kuipers, B., 1978. Modelling Spatial Knowledge, In

Cognitive Science, 2, 129-153.

Freksa, C., Moratz, R., and Barkowsky, T., 2000.

Schematic maps for robot navigation. In C. Freksa, W.

Brauer, C. Habel & K. F. Wender (Eds.), Spatial

Cognition II – Integrating abstract theories, empirical

studies, formal models, and practical applications (pp-

100-114), Berlin: Springer.

Moravec, H.P., and Elfes, A., 1985. High Resolution Maps

from Wide Angle Sonar. In Proc. IEEE Int’l. Conf.

Robot. and Automat., St Louis, 116-121.

Thrun, S., Fox, D., Burgard, W., Dellaert, F., 2001. Robust

Monte Carlo localization for mobile robots. In

Artificial Intelligence 128 (299-141), Elsevier.

Thrun, S., 2002. Robotic mapping: A survey. In

G.Lakemeyer and B. Nebel, editors, Exploring

Artificial Intelligence in the New Millenium. Morgan

Kaufmann.

Thrun, S., 1998. Learning Metric-Topological Maps for

Indoor Mobile Robot Navigation. In Artificial

Intelligence, Vo. 99, No. 1, 21-71.

Escrig, M.T., and Peris, J.C., 2005. The use of a reasoning

process to solve the almost SLAM problem at the

Robocup legged league, IOS-Press, Catal. Conference

on Artificial Intelligence, CCIA’05, Oct. 2005.

Zimmermann, K., and Freksa, C., 1993. Qualitative spatial

reasoning using orientation, distance, and path

knowledge. Proc. Of the 13

Inter Joint Conf. on AI

Workshop on Spatial and Temporal Reasoning.

ICINCO 2006 - ROBOTICS AND AUTOMATION

234