1999problems and solutions in acquisition and interpretat ion of sensorial

7/25/2019 1999Problems and Solutions in Acquisition and Interpretat Ion of Sensorial

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Problems and Solutions in Acquisition and Interpreta t ion

of

Sensorial

Data on

a Mobile Robot

Giovanni Agazzi Andrea Bonarini

IDSIA

Gorso

Elvezia

36,

Lugano, Switzerland

e-mail: [email protected]

Dipartimento

di

Elettronica e Informazione

Politecnico di Milano

Piazza Leonard0 da Vinci

32

Milano, Italy

e-mail: bonarini@elet polinni it

Abstract

e discuss so me guidelines to cope w ith problecms

that ar ise when us ing cheap and s imple sensor on

nio

bile autonomou s robotic agents . I n part icular we

fo-

cus o n the perceptual al ias ing problem und o n the pas-

s ibi l ity to per fo rm active sensor data acquis it ion. W e

presen t a robotic architecture tha t we have implem ented

on a real robot following the proposed guidelines. Th e

obtained mobile robot satisfies the design specifications

nauiyutiny avtmiomously in u unstructured e7vviron-

m ent .

1 Introduction

In this paper we describe

a

control architecture for

a mobile rohot that effectively integrates several ap-

proaches to interpret sensorial data, solving some of

the problems arising when using low-cost and simple

sensors. Aim of thi s paper is to present some guide-

lines useful to cope with these problems, in particu-

lar, perceptual aliasing [13] The robotic architecture

we present in the paper exemplifies how to implement

these guidelines and it also provides some experimental

support to them. It has been conceived for a mohile

autonomous agent which operates in a real, complex

and unstructured environment. Effective exploitat (on

of the sensorial system is critical in thi s applicaticxi.

Any control strategy aims at realizing a mapping De-

main components of our architecture, showing how

they implement the guidelines. A basic element of our

approach is active perception. In section

6,

we present

some of the main interpretations of this term thal, can

be found in literature and their implementation in our

architecture. In th e conclusion we d.iscuss th e impor-

tance of a comprehensive approach

to

the problem of

interpreting poor sensorial data.

2 Perceptua l aliasing

Perceptual aliasing is the problem t hat arises when

either a particular state of th e world maps

to

several

states in the internal representation,

or

a single in-

ternal state corresponds to multiple world states

[13].

The first kind of problem is stronger when the agent

can observe the world with several dlegrees

of

freedom,

since this may produce a large number

of

represen-

tations which describe under different viewpoint

3

the

same world st ate. The second kind uf problem oIucurs

when th e agent has limited perceptual1 capabilities with

respect to the complexity of the environment where it

operates. The number

of

world states is large and the

information available to discriminat

e

among them is

poor. The problem arises when th e control strategy

should be different in different worId states that are

perceived as the same internal state.

tween the state of the agent and its actions. Thus,

identification of the agent state is a main issue.

In t he next section, m7e introduce the perceptual

aliasing problem, and we discuss the typology of l,he

underlying problems. In th e third section we present

some general strategies which are effective to cope with

perceptual aliasing problems. Then, we analyze ,he

Different interpretations of the perceptual aliasing

problem have been proposed in literature. Some of

them focus on the problem of mapping several world

state s to one internal state

[ l l ]

Other approachee con-

cent rate on the problem of discriminating among dif-

ferent world s tates tha t are described by the same im-

mediate internal stat e

171

[9].

0-7803-5276-9/99/$10.00 999

IEEE

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3 Guidelines

World s tates select ion. The concept of deictic rep-

resentation [1][6], uggests to consider only t he world

states that are useful to complete the task, avoiding

unnecessary complexity. A reduction in the number

of world stat es simplifies the problem of representing

the relationship between world states and perceptual

states.

Having selected

a

set

of relevant world states, it is important to identify

the only perceptual data needed to distinguish among

them. The others may be hardly mapped t o the world

states.

Standardiza t ion

of

perception condit ions .

By realiz-

ing standard gaze configurations towards the observed

objects, it, is possible to reduce th e number of percep-

tual states. It is important to choose a significant ob-

serving perspective, to reduce the ambiguity between

world states that may seem similar from other points

of view. Tight collaboration between action and per-

ception maintains the agent in the best conditions for

th e perception. As better described later, this is a case

of active perception.

Ef ic ien t chs s i f i cu t ion .

Categorization of perceptual

da ta has to be flexible, robust and accurate. Flexi-

ble, because it has t o describe the several perceptual

states corresponding to each world state. Robust, to

recognize the similarity between mapped and percep-

tua l stat es, while the lat ter are affected by noise. Accu-

rate, taking into account similarity without confusing

different world state s. Ability to recognize similarity

must not affect the capability t o remember exceptions.

A

small difference in a measure pattern may be

a

noise

effect or a significant exception. These two cases should

be classified as different.

Hierarchical perceptual eflort.

Certain world states

are critical for the success of th e agent. When th e

internal state looks critical, the agent devotes an ex-

tr a effort t o collect information t o improve recognition.

This activity may also provide information useful for

particular actions to be done in the critical state. The

perceptual effort is hierarchical because time-expensive

activities take place only when needed, while a super-

ficial percept,ion is enough for a fast exploration of the

environment.

Perceptual data selection.

4 The robotic architecture

The aim

of

this architecture is to provide an au-

tonomous agent, equipped with low-cost sensors, with

the capability to explore an unknown environment.

The goal of exploration is to find spatial elements that

could be useful to accomplish a task. In the experi-

ment we are presenting, the environment is

a

generic

corridor, and relevant spatial elements are open doors,

and tu rns. Th e task we are facing consists of passing

through th e nth open door. Along the corridor sev-

eral unexpected elements may be present, either sta tic

(protrusions, automatic dispensers),

or

dynamic (peo-

ple, other robots). The sh ape of the corridor is generic,

and it may include corners. The agent explores the

corridor following one wall. Arriving at the end of the

corridor, th e robot is able t o manoeuver and come back

to explore the other wall. T he agent may measure the

distance to elements

of

the environment in a limited

number of directions 12 in our exam ple), using sonar

sensors. These sensors a.re a.ffect,edby errors a.nd inac-

curacy.

The description of a robot>ic rchit-ectme may start

from different points of view

[2].

Here, we focus on re-

active behaviors and deliberative strategies. In reactive

behaviors, the world model is implicit, being intrinsi-

cally implemented in the modality of interaction with

th e environment. On th e contrary, deliberative control

strategies are based on an explicit representation of the

world, obtained from perceptual dat a by an abstraction

process [2][4]

We are proposing an architecture that follows some

guidelines borrowed from the Brooks subsumption

archte cture[4]. Our control system is partitio ned in

modules, each implementing a functionality of the

agent. The system may grow incrementally by adding

new functional layers. Each module and layer can be

tested independently from th e others. We integrate

modules implemented following this approach, with

spatial element classification performed by behavior

modules implementing active perception strategies.

We have built our architecture, by incrementally

adding the following modules:

e q d o r a t o r

(low level

navigation),

classifier

(spatial element recognition), in-

teresting object inves t igator (active perception), ac-

curate manoeuvrer (predefined movements to be ac-

tivated), navigator (high level navigation).

Firstly, we have implemented on the agent the re-

active behavior to explore a reference wall, by: pro-

ceeding

at a

given average distance, avoiding obsta-

cles, and managing unexpected collisions. Th e archi-

tecture of this module is inspired to t he one presented

by Mataric [S. The mobile agent controlled by this

reactive module is able to complete the explora#tion

path in different corridors, in presence of dynamic and

stat ic obstacles. After manoeuvring to avoid obstacles,

th e requested average distance t o t he reference wall is

quickly restored. Th e exploration module has only im-

plicit knowledge about the environment.

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The classifier module has been added to the robat

when this was already able t o explore walls. Aim of this

module is to recognize spatial elements, interpreting

distance measures acquired by sonar sensors. Neither

information about past data, nor about robot move

ments

is

taken into account. By this module, th e agent

is able to detect spat ial elements th at are relevant to

deal with its task. In the experimental case we are pre-

senting, we have two relevant elementas:walls and doc~r

jambs. The classifier compares the information coni-

ing from sensors with a representation of the relevarit

elements learnt off-line by

a

fuzzy neural network, an

implement,ation of Fuzzy ARTMAP

[5]

discussed

be-

low.

When the robot recognizes an element interesting

for the task to be done

-

an open door in this case

-

the

object investigator module acts to collect detailed in-

formation about the interesting object. In other t,erui:3,

the classifier module performs a fast (and possibly u11-

reliable) detection of relevant elements, and the inves-

tigator actively explores the object. The exploration

procedure implies a synergy between action and pe.r-

ception. Basing on a priori knowledge about the geo-

metrical structure of the object, an exploration path is

planned. During the course, fusion of different kinds of

sensorial information allows to check the object geome-

try. The a priori geometrical structure is supplemented

with quantitative data dynamically acquired; in this

case, door length and orientation of the exploraticm

path. Acquired information

is

useful

for

two purposes.

First,

it

is possible to check whether the explored ob-

ject corresponds to th e one recognized by th e classifier.

Then, an accurate motor skill can be instant,iated,

to

lead the agent in a given position, with respect to the

object.

After having collected metrical information about

th e interesting object, t,he agent is able to plan its next

action. Detailed da ta allow to reliably establish the

agent position. In the particular case of our exper-

iment, possible environmental situations are an open

door, a convex

90

degrees turn, or a false categoriza-

tion. Coping with these cases requires accurate move-

ment procedures. During th e execution of these pro-

cedures, we have a feedback control on th e moveme:nt

tha t allows to accurately follow a given path. Pa ti s

are planned by the agent using metrical information

acquired during active perception procedure. Dynanii-

cally acquired representative knowledge is used jointly

wit,h a priori known geomet,rical models t,hat, describe

the object a nd th e robot kinematics.

Th e ability to recognize interesting spatial elemenls,

like the open door or

t he

90 degrees turn, allows to

realize a minimal navigation plan.

It

is possible to sie-

lect natural landmarks tha t allow self-ljocalization. The

description of navigation plans based on landmarks is

robust and flexible, and matches th e att itu de of the hu-

man designer. Moreover, the success of th e navigation

does not depend on the accuracy of

a

geometrical de-

scription but only on landmark recognition.

A

trivial

example of such kind of plans may be: ,enter in th e

nth open door detected in

a

corridor.

Discussion

Our robotic architecture deals with perceptual afias-

ing problem in both directions. As mentioned above,

th is architecture is composed by a set of modules. Each

module implements a behavior. Decisions regarcling

how to act a re taken at different levels. There are three

main kinds of knowledge involved. Implicit knowledge

drives reactive behaviors, while stat LCrepresentative

knowledge is used to qualitatively deacribe spatial el-

ement features. Active perception allows to collect a

third kind of knowledge, that gives a precise description

of spatia l elements. This distinction is important be-

cause perceptua l aliasing affects thesc. kinds

of

knowl-

edge in different ways.

The

ow

level explor tion behavior is purely reac-

tive. It drives the robot along walls at a given distance,

avoiding collisions and manoeuvring to t urn around ob-

stacles. As in many reactive approaches, data coming

from each sensor are considered separately, without any

sensor fusion activity since each da ta has a meaning by

itself. This behavior takes into account the distaiices

measured by sonar sensors and th e sta te of th e collision

detectors (bumpers). When a bumper reports a colli-

sion, depending on which side it belongs, there is only

one proper action to do. The strategy is not

so

simple

for th e sonar distances, but t he idea is similar. For

each distance, depending on the direction it is taken,

there are three ranges of values. If th e distance is over

a

threshold, the distance is not relevant. Under this

threshold there are two ranges: objects detected in the

outer range a re considered relevant those in the inner

range are considered dangerous. This criterion maps

all the distances to three relevant states. The move-

ment decision considers only two states for each d irec-

tion. This approach copes with the perceptual aliasing

problem through a massive simplificat,ionof th e ovc?rall

information. Only the sensors able to supply informa-

tion useful to deal with the task are taken into account.

Th e problem related with t he one t o many relationship

between world states and perceptual states is strongly

simplified. We do not consider explicitly th e opposite

point

of

view, where different world st ate s generate in-

distinguishable internal states. Here, this problem is

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not critical, because the reactive system is able to dis-

criminate among the states that are relevant to solve

its task.

The

classafier module

should classify sensor da ta and

try to match the classification with its world model.

This process has to be very fast, since the agent should

not st op during exploration. The decision of perform-

ing an active perception behavior in presence of an

interesting spatial element must be taken before the

agent passes over. As described above, a way to reduce

perceptual aliasing is to accurately choose which sta tes

have to be recognized, in order to complete the task.

In our case, the agent should decide whether keeping

on exploring the environment or performing an active

perception behavior. Thus, it is important to distin-

guish states where the searched spatial element may

be present from states where the environment does not

present any relevant feature. In our example, the aim

of th e exploration is to find an open door. A relevant

spatial element is the door jamb. It is possible to limit

the set of th e inte rnal model classes to a couple of sit-

uations: th e agent is beside a wall and the agent is

mar a door jamb. This simplifies the world mapping

problem .

In an unstructured environment, data concerning

the relevant environmental elements are usually mixed

with uninteresting and disturbing dat a. Moreover,

when performing an exploration task, relevant elements

can be found in any direction. Therefore, it is interest-

ing to have a way t o detect t he presence of th e searched

element,independently from its position and the com-

bination with other elements. Therefore, instead of

considering the entire perceptual state, it is possible to

focus the attention only on subpa rts

of

it, in order

to

find signs

of

the presence of th e searched element. This

strategy reduces the complexity of the categorization

task. Considering only selected portions of the per-

ceptual space, th e number and complexity of possible

perceptual st ates decreases. This simplifies th e prob-

lem

of mapping th e relationship between an interesting

world sta te and all th e corresponding perceptual states.

The agent we have devised to test this architecture,

can measure distances using sonar sensors mounted

on a rotat ing turret . It is possible to collect

12

dis-

tances on t he whole

360

degrees horizon, in

a

reason-

able amount of time. Instead of considering the entire

set, the categorization module extracts

12

windows,

each one composed by

4

measures from close sensors.

Each window represents a

120

degrees slice of the hori-

zon, gazing to

a

different direction. The analysis

of

this da ta window allows to recognize th e basic elements

we are interseted in. Classification is made on all the

windows, scanning the horizon to search for a relevant

spatial element, independently of the relative position

with respect to the agent and the presence of other el-

ements. The key point of this type of approach is the

reliability of th e classification procedure.

Selection of the relevant world states and focaliza-

tion of the perceptual attention reduce the complex-

ity

of

th e classification problem. However, th e prob-

lem

of

classifying perceptual windows is not trivial.

Data

noise draslically iricreases

the

range or

percep-

tual states that the agent can observe in a given world

state . A fast classifier is needed, able to map a large

number of perceptual states t o the logical states th at

are useful to perform the task. In our architecture, we

have implemented Fuzzy ARTMAP, a neuro-fuzzy net-

work. During a supervised training phase, this network

learns an effective representation of the many-to-one

mapping from the world state space

to

the perceptual

object space. This network is also able to perform ap-

proximate matches, thus coping with noisy measures

and the diversity of spatial elements in the real world.

The need for a supervised training phase is a limit,

since it requires a skilled supervisor and off-line train-

ing, but it is also an element of flexibility, giving the

possibility

to

LraiIi the network

to

adapt

the

agent to

different environments. Another remarkable feature of

Fuzzy ARTMAP is its accuracy.

It

is able to take into

account similarity between patterns, without discard-

ing exceptions. A small difference in a measure patte rn

may be either a noise effect

or a

significant exception.

The last, important module, from th e point of view

of perceptua l aliasing, implements the hierarchical per-

ceptual act iv i ty . A deeper examination of t he environ-

ment is realized by active perception that gives the

possibility t o recognize reliably the real state . This

methodology effectively copes with the problem related

to ambiguity of real states, and it does not affect agent

performance during exploration. In fact , active percep-

tion takes place only when the classifier recognizes a n

interesting element. Data collected by the active per-

ception procedure may be useful to plan future actions.

To better explain the several implications connected to

active perception, we dedicate the next section to this

argument.

6 Active perception

The term "active perception" has different meanings

in literature. We consider two of them. The first has t o

do with focusing the sensorial system while observing

the environment. The second is implemented by the

active exploration

of

the target, aimed to obtain an ef-

fective recognition and t o collect detailed data. Active

exploration enhances stat e identification th rough cate-

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gorization of motor/sensor data. The collected, quam

tita.tive information is used t o plan subsequent action:;

concerning the explored object.

In this case the active part of percep-

tion consists in positioning the agents sensors toward:$

the target [3]

[ll].

Appropriate positioning makes eas-

ier the interpreta tion of the perceptual sta te. Look-

ing to an object from a fixed point of view reduces

the variety of sensorial sta tes corresponding to each

world state. Standardization of perceptual gaze is use-

ful also when categorization is based on knowledge ac-

quired by a learning process. It increases the prob-

ability that learned sensorial states be similar to tht:

stat.es observed lat,er. Another benefit. relat,ed to thi:;

kind of active perception is the possibility to select

t

standard data acquisition procedure (e.g., a particu-

lar point

of

view), focusing on the target features that

make it different from

a

similar object. This kind of ac-

tive perception allows to simplify the perceptua l alias-

ing problem in both its aspects. In the example of our

mobile robot, this strategy takes place during explo-

ration . The exploration behavior has the additiona.1

task of maintaining th e agent parallel to the wall at

predefined distance. This allows to obtain by the sonar

sensors homogeneous patterns of measurements. After

manoeuvring to avoid obstacles or to follow corridor

shape , the agent restores the deisired alignment a s soon

as

possible. A sub-module is dedicated to achieve and

maintain th e correct alignment. The low level explo-

ration module tells the sub-module when the situatio:i

is correct, and no strong steering correction is needed..

Then, the sub-module starts a strategy that allows tt3

obtain an accurate alignment by considering data corn-

ing from the two sonars directed towards the wall. T he

probability to obtain a valid measure is enhanced b,y

using two different incidence angles, each of 15 degrees

on each side of the robot axis. Taking into account the

smaller of these two da ta , it is possible to reliably know

the distance from the wall (see figure 1 . Comparing

step by st ep th e distance values, it is possible t o irL,-

plement a feedback on the steering action. While the

exploration behavior is designed to cope with majar

turns to avoid obstacles, the sub-module makes small

corrections.

Active exploration of the target.

The first aim

of

the

active exploration module is to verify the identity of

the object that has been classified during exploration.

Many approaches can be found in literatu re t o cope

with the state identification problem.

A

common fest-

ture is the use of history information t o uncover hidden

states . The world model is not defined on the immedi-

ate percepts, but by a combination of the current per-

cepts with

a

short-term memory of past percepts and

Focusing.

actions. Many examples of this kind of technique are

presented in papers regarding Reinforcement Learning

[7] [9]. In fact, aim of Reinforcement Liearning applied

to this field is to build a good mapping between states

and actions. Thus, a reliable identification of the stiite

is needed. Definition of in terna l states representing in-

formation about past percepts and actions may be seen

as a way to reduce the ambiguity of the identificat.ion

of

the current sta te. Start ing from a different point

of

view, we have designed a procedure for stmateden-

tification. This strategy takes place only in situations

tha t are important to achieve the agents goal. Dur-

ing these sil.uat ans the active perception requires an

additional perceptual effort that slows down the agent

performance, bu t allows to collect more acc urate infor-

mation about) the world state . Data are collected by a

procedure that makes the agent to actively explore the

target. A priori knowledge about target shape is used

t o plan movements that enable the robot t o explore

it. The tight, collaboration between sensor and motor

systems makes it possible to acquire a iso quan titativ e,

metric information.

In the case of our mobile robot, the exploration

movement is driven by the analysis of data coming from

sonar and odometric sensors. When t he classifier msd-

ule detects that a door jam b is present, the robot stops.

It stops approximately near the jamb and parallel t o

the door threshold. The turret tha t carries sonar sen-

sors rotates to a position from which it is possible to

measure distances orthogonally to th e robot axis. J.he

agent starts to move forward. An accurate feedback is

applied to the steering control, using odometric inIor-

mation, in order to obtain a straight line path.

The

sonar sensor that is hortogonal to the motion trajec-

tory collects a large number of data. Keeping the tur-

ret still, it is possible to increase the frequency of da ta

acquisi tion. Th e large number of son.ar da ta plot an

accurate profile of the explored object,. In the case of

an open door, the procedure verifies if distances are

compatible with the presence of such kind of spatial el-

ement. When th e exploration path leads the agent near

the second jamb, distances acquired by sonar suddenly

change. The whole set of da ta describe the door ge-

ometry, the position of the robot and its orientat,ion

towards the door. Odometric data give the distance

covered between the two jambs. Somar data give the

distance from each jamb and the straight line path.

Taking into account t,he geometrical model

of

the sonar

sensor, we have a complete description of the situation.

Thus, it is possible to classify the door by its breadth.

If the exploring path proceeds over a fixed maximhum

length, the agent is probably beside a convex 90 de-

grees turn of the corridor, and can thus distinguish it

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0 100

z

300 600 500

Figure 1 Experimental data: path followed

by the robot and sonar images

on

the wall a-

b wall exploration, b-c active perception, c-d

pass through manoeuvre)

from

a

door.

In Figure

1

you may see the actual data taken from

our robot executing a movement that fits morphologic

properties of the examined object . The movement al-

lows to acquire quantitative data . These data cover

several aspect,s, integrating different approaches that

is possible to find in litera ture. In fact, categorization

of t he world state takes into account motor/sensorial

data

[12][11].

The

ih

priori geometric model is used to

plan an exploration path which supplements the model

with quantitative data [2].

Information acquired dur-

ing this process is necessary to do successive actions

involving the explored object

[ lo]

7

Conclusions

The robotic architecture we have presented shows

how it is possible to build an organic approach to

problcms rclatcd to acquiring and intcrprcting scnso-

rial data . Considering an autonomous agent in

a

real

environment is a significant example, because it fits

to an integrate solution like the one we are proposing.

Considering a mobile robot is important to underline

the role played by active perception. This strategy

arises from the idea of se tting a tight collaboration be-

tween sensor and motor systems: agent knowledge may

be increased involving motor capabilities, while

a

bet-

ter knowledge

of

the world allows to enhance action

prospects.

The architecture we propose is open to several in-

teresting developments. I t would be interesting to give

the classifier the possibility t o autonomously recognize

the categories, depending on sonar state diversifica-

tion. For example it is possible to generate such kind

of

categories using Fuzzy ART, a sub-module of the

fuzzy

logic neural

network we

have

implemented.

Ail

tonomously recognized categories may

be

meaningful

for the autonomous agents tasks. We plan to include

other landmark elements to increase the agent possi-

bilities. According to th e features of the new elements,

other perception strategies may be designed. A large

set of landmark types can be exploited to provide (and

learn) rich topological maps, making it possible to in-

teract with the agent close to th e human designers at-

titude.

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