OBSTACLE DETECTION IN MOBILE OUTDOOR ROBOTS

A Short-term Memory for the Mobile Outdoor Platform RAVON

H. Sch

afer, M. Proetzsch and K. Berns

Robotics Research Lab, University of Kaiserslautern, P.O. Box 3049, D-67653 Kaiserslautern, Germany

Keywords:

Obstacle Detection, Obstacle Avoidance, Behaviour-based Navigation, Mobile Robotics, Short-term Memory,

Distributed Minimal World Model, Algorithms, Biologically Inspired Robotics.

Abstract:

In this paper a biologically inspired approach for compensating the limited angle of vision in obstacle detection

systems of mobile robots is presented.

Most of the time it is not feasible to exhaustively monitor the environment of a mobile robot. In order to

nonetheless achieve safe navigation obstacle detection mechanisms need to keep in mind certain aspects of

the environment. In mammals this task is carried out by the creature’s short-term memory. Inspired by this

concept an absolute local map storing obstacles in terms of representatives has been introduced in the obstacle

detection and avoidance system of the outdoor robot RAVON. That way the gap between the ﬁelds of vision of

two laser range ﬁnders can be monitored which prevents the vehicle from colliding with obstacles seen some

time ago.

1 INTRODUCTION

In recent years complex mobile robotic systems have

been developed for autonomous navigation in various

environments. The more complex a robot’s working

place gets the more sophisticated its obstacle detec-

tion and avoidance facilities need to designed. First

of all, sensor equipment must be appropriate for the

requirements of given scenarios. In most cases such

sensors have a limited ﬁeld of vision and therefore

cannot cover the complete area around the robot. One

possibility to overcome this limitation is mounting

several devices of the same kind. However, this leads

to higher weight and costs and the evaluation of more

data might exceed the computational capacity.

In mammals the short-term memory is used to

compensate for the relatively small ﬁeld of vision.

The hindlegs of a cat for example have to avoid ob-

stacles some time after the cat has visually perceived

the hindrance (McVea and Pearson, 2006). Clearly

the cat has to keep in mind certain information about

its local environment in order to safely navigate com-

plex terrain.

In this paper an approach for adopting the princi-

Figure 1: The outdoor robot RAVON in rough terrain.

ple of a local short-term obstacle memory for mobile

outdoor robots is presented. The implemented sys-

tem is used to gather additional information for the

behaviour-based control system of the robot RAVON

(Robust Autonomous Vehicle for Outdoor Naviga-

tion, see Figure 1). For now data of two laser scan-

ners – mounted at the front and the rear of the robot

– is evaluated and inserted into the obstacle memory.

141

Schäfer H., Proetzsch M. and Ber ns K. (2007).

OBSTACLE DETECTION IN MOBILE OUTDOOR ROBOTS - A Short-term Memory for the Mobile Outdoor Platform RAVON.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 141-148

DOI: 10.5220/0001630701410148

 SciTePress

The approach, however, is kept in a generic way such

that additional sensor systems as e.g. stereo camera

systems can easily be integrated.

The outline of the paper is as follows: In the

next section work related to the topic of this paper

is described. Afterward we give some details on

the outdoor robot RAVON which is used for vali-

dating the presented approach. Section 4 describes

the concept of the short-time memory including some

details about the obstacle detection methods. Sec-

tion 5 shows an experiment used for evaluating the

presented approach. Finally, we conclude with a sum-

mary and directions for future work.

2 STATE OF THE ART

Obstacle avoidance in outdoor terrain is a topic of

several publications. Mostly there is the distinction

between reactive obstacle avoidance and building up

complete geometric maps. Reactive approaches like

(Badal et al., 1994) compute steering vectors ac-

cording to the proximity of obstacles in the current

view. However, for vehicles supporting agile steer-

ing manoeuvres like sideward motion this neglects

the problem of possible collisions outside the ﬁeld of

view. Other similar approaches ((Kamon et al., 1996),

(Laubach and Burdick, 1999)) add some kind of state

to the obstacle avoidance system but do not explicitly

consider positions of hidden obstacles.

Work describing building up maps which contain

information about terrain elevation ((Shiller, 2000),

(Bonnafous et al., 2001)) also neglect the need to en-

large the virtual ﬁeld of vision. A high degree of com-

putation is used to evaluate the traversability of the

detected terrain region and to calculate a feasible tra-

jectory. However, in outdoor terrain given paths can-

not be followed precisely due to disturbances. There-

fore, an evaluation of possibly dangerous obstacles

needs to be undertaken during the driving manoeuvre.

The approach presented here can be seen in be-

tween the completely reactive and the mapping ap-

proach. A short-term memory keeping only the rele-

vant information deals as the source for a behaviour-

based system keeping the robot away from currently

relevant obstacles.

3 VEHICLE DESCRIPTION

The platform used to examine obstacle avoidance is

the four wheeled off-road vehicle RAVON (see Fig-

ure 1). It measures 2.35 m in length and 1.4 m in

width and weighs 400 kg. The vehicle features a four

Figure 2: Regions monitored by the obstacle avoidance fa-

cilities.

wheel drive with independent motors yielding maxi-

mal velocities of 3 m/s. In combination with its off-

road tires, the vehicle can climb slopes of 100% incli-

nation predestining it for the challenges in rough ter-

rain. Front and rear axis can be steered independently

which supports agile advanced driving manoeuvres

like double Ackerman and parallel steering.

In order to navigate in a self-dependent fashion,

RAVON has been equipped with several sensors. For

self localisation purposes, the robot uses its odometry,

a custom design inertial measurement unit, a mag-

netic ﬁeld sensor, and a DGPS receiver. The sensor

data fusion is performed by a Kalman ﬁlter (Schmitz

et al., 2006) which calculates an estimated pose in

three dimensions.

In order to protect the vehicle in respect to ob-

stacles, several safety regions are observed by dif-

ferent sensor systems (Sch

afer and Berns, 2006) (see

Fig. 2). First of all, hindrances can be detected using

the stereo camera system mounted at the front of the

vehicle. This obstacle detection facility is comple-

mented with two laser range ﬁnders (ﬁeld of vision:

180 degrees, angular resolution: 0.5 degrees, distance

resolution: about 0.5 cm) monitoring the environment

nearby the vehicle. Data from both sources of prox-

imity data is used for obstacle avoidance by appropri-

ate behaviours, the fusion of which is performed in-

side the behaviour network (Sch

afer et al., 2005). In

case of emergency, the system is stopped on collision

by the safety bumpers which are directly connected to

the emergency stop to ensure maximal safety. In the

future, the compression of the spring system shall be

used to detect occluded obstacles in situations where

geometric obstacle detection cannot be used.

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142

Figure 3: Top-level concept of RAVON’s Obstacle Avoid-

ance Facility.

4 A SHORT-TIME MEMORY FOR

RAVON

In order to compensate for the blind angles of the sen-

sor systems local short-term memories shall succes-

sively be introduced. Any of these can be regarded as

a virtual sensor system which can seamlessly be inte-

grated into RAVON’s control system as described in

(Sch

afer et al., 2005). That way step by step blind

regions can be covered with increasing accuracy as

overlapping virtual sensors yield more and more ad-

ditional information. In this paper the validation of

the general approach and connected technical facili-

ties are presented by the example of closing the gap

between the two laser range ﬁnders (See Figure 2).

Listing 1: Declarations.

# Raw s e n s o r i n f o r m a t i o n fro m t h e

# l a s e r r a ng e f i n d e r s

s c a n n e r

d a t a

# FIFO c o n t a i n i n g t h e c a n d i d a t e o b s t a c l e s

o b s t a c l e f i f o

# The m aximal l e n g t h o f t h e q ueue

# a s s u r e s t h a t t he o b s t a c l e l i s t s

# h ave a c e r t a i n age when r e t r i e v e d .

# F u rt h e r m o re t h e s c a n s a r e d i s c a r d e d

# i f t h e v e h i c l e i s n o t moving t o p r e v e n t

# t h e a c c u m u l at i on of o u t d a t e d i n f o r m a t i o n .

m a x n u m be r o f sc a n s

# S t o r e s t h e c u r r e n t r o b o t po s e i n t h e

# r o b ot ’ s a b s o l u t e wo r k i n g c o o rd . s y s t e m

c u r r e n t r o b o t p o s e

# S t o r e s t h e c u r r e n t v e l o c i t y of t h e r o b o t

c u r r e n t v e l o c i t y

# S t o r e s r e p r e s e n t a t i v e s i n t h e b l i n d

# a ng l e t o t h e l e f t o f t h e r o b o t .

s h o r t t e r m m e m o r y l e f t

# S t o r e s r e p r e s e n t a t i v e s i n t h e b l i n d

# a ng l e t o t h e r i g h t o f t h e r o b o t .

s h o r t t e r m m e m o r y r i g h t

# T h r e s ho l d o f t h e m ax imal e u c l i d e a n d i s t a n c e

# b e t ween t h e c u r r e n t v e h i c l e p o s e a nd t h e

# on e a t w hich a s c an was t a k e n .

p r o g r e s s t h r e s h o l d

Figure 3 illustrates the structure of the laser-

scanner-based part of the obstacle avoidance system.

The sensor ﬂow

of the main loop which is dealing

with the short-term memory is outlined in Listing 2

(For explanation of the particular variables used see

Listing 1). The ﬁrst step of the main loop is the de-

tection of obstacles from the laser range data which is

carried out in the Obstacle Detection facility. The cur-

rent vehicle pose in the robot’s absolute working coor-

dinate system is applied to the resulting obstacle lists

in order to yield an absolute reference map through

which the robot can virtually be navigated. After that

the lists are casted into sector maps which are used

by the Obstacle Avoidance behaviours to com-

pute evasive steering commands if necessary.

For the extension described in this paper a FIFO

queue was introduced which holds in stock the ob-

stacle lists of several subsequent scans. In every

sense cycle older obstacle lists are examined for ob-

The control concept underlying all rob ot projects at the

Robotics Research Lab strictly separates the main loop into

a buttom up sense and a top down control cycle which are

alternately invoked.

OBSTACLE DETECTION IN MOBILE OUTDOOR ROBOTS - A Short-term Memory for the Mobile Outdoor Platform

RAVON

143

stacle representatives which may have migrated into

the blind angles. Older in this sense can be interpreted

in two ways: a) the scan was taken a certain time ago

(age criterion

) or b) the scan was taken a certain dis-

tance ago (progress criterion

Listing 2: Main Loop.

Sen s e ( )

# D e t e c t o b s t a c l e s f rom r a n g e i n f o r m a t i o n

D e t e c t O b s t a c l e s ( s c a n n e r d a t a ,

o b s t a c l e l i s t f i f o )

i f ( c u r r e n t v e l o c i t y != 0) do

# remove r e p r e s e n t a t i v e s w hi ch a r e no

# l o n g e r i n t h e s c a n n e r ’ s b l i n d a n g l e

Tri m ( s h o r t t e r m m e m o r y l e f t , c u r r e n t r o b o t p o s e )

Tri m ( s h o r t t e r m m e m o r y r i g ht , c u r r e n t r o b o t p o s e )

# c h ec k age c r i t e r i o n ( a )

w h i l e ( S i z e ( o b s t a c l e l i s t f i f o ) >

m a x n u m ber o f s c ans ) do

o b s t a c l e l i s t = F i r s t ( o b s t a c l e l i s t f i f o )

F i l l O bstac l e M e m o r y ( s h o r t t e r m m e m o r y l e ft ,

s h o r t t e r m m e m o r y r i gh t ,

o b s t a c l e l i s t ,

c u r r e n t r o b o t p o s e )

R emo v e Fir s t ( o b s t a c l e l i s t f i f o )

# c h ec k p r o g r e s s c r i t e r i o n ( b )

o b s t a c l e l i s t = F i r s t ( o b s t a c l e l i s t f i f o )

w h i l e ( D i s t a n c e ( P ose ( o b s t a c l e l i s t ) ,

c u r r e n t r o b o t p o s e )

> p r o g r e s s t h r e s h o l d ) do

F i l l O bstac l e M e m o r y ( s h o r t t e r m m e m o r y l e ft ,

s h o r t t e r m m e m o r y r i gh t ,

o b s t a c l e l i s t ,

c u r r e n t r o b o t p o s e )

R emo v e Fir s t ( o b s t a c l e l i s t f i f o )

o b s t a c l e l i s t = F i r s t ( o b s t a c l e l i s t f i f o )

e l s e

# d i s c a r d o l d e r s c a n s i f n o t moving

w h i l e ( S i z e ( o b s t a c l e l i s t f i f o ) >

m a x n u m ber o f s c ans ) do

R emo v e Fir s t ( o b s t a c l e l i s t f i f o )

e s l e

esn e S

If one of the two criteria applies to an obstacle list

FillObstacleMemory checks the obstacle rep-

resentatives and places them into the corresponding

short-term memory. In order to prevent the short-

term memory from overﬁlling Trim (See Listing 4)

removes obstacle representatives which are no longer

in the robot’s blind angle. Furthermore the obstacle

list FIFO has a limited maximal size. That way only

a limited number of scans is keept in mind and is ex-

amined which minimises memory consumption and

computational expenses.

As the Obstacle Sector Maps the short-

term memories are evaluated by the Obstacle

Avoidance component which computes evasive

steering commands and inhibits the Homing be-

haviours if applicable. The steering commands

The age of a scan is modelled implicitly by the maximal

number of scans in the queue.

The progress threshold assures that no large gaps oc-

cur in the obstacle memory at higher speeds as scans are

examined earlier.

Figure 4: Screenshot showing clustering of detected range

values (top) and photo showing the scenario (bottom).

from the Homing facility and the Obstacle

Avoidance are fused according to the behaviour ar-

chitecture described in (Proetzsch et al., 2005). If no

obstacles are present Homing successively navigates

along a trajectory given in subsequent target poses

provided by the Navigator component.

4.1 Obstacle Detection

The range data from the laser scanners is ﬁrst of all

clustered according to point-wise adjacency in order

to ﬁlter artefacts and entities which are likely to rep-

resent ﬂexible vegetation (See Figure 4). In this ﬁgure

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144

the current laser scanner data (dark points) are inter-

preted as clusters (red boxes) according to their dis-

tance. Each of the clusters is then evaluated concern-

ing its dimension and its number of cluster represen-

tatives. If both criteria are true clusters are interpreted

as obstacles.

In order to have a uniform interpretation of near

obstacles which can be used by obstacle avoidance

behaviours the sensor coverage area is divided into

sectors. In Figure 4 the closest obstacle in each of

these sectors is indicated by a green line. In Figure 4

the human to the left and the bushes to the right of the

robot have clearly been detected as obstacles. Note

that the bushes are subject to severe noise and that

they are therefore detected as a large number of small

ﬂuctuating objects. For that reason obstacles cannot

be correlated over time which is a problem for local

map building as representatives accumulate over time

if the robot is moving slowly. Using an occupancy

grid would be a solution to this problem but it lacks

accuracy because of rasterisation. Furthermore the

consideration of objects moving independently from

the vehicle – which shall be introduced in the future –

would be complicated a lot.

4.2 Short-term Memory

As already indicated above obstacles detected in laser

rage data consist of points which represent the bound-

ary of a rigid object. Thus in every sensor cycle a list

of such point clusters

is generated and stored in a

FIFO queue for further processing. To every obstacle

list the obstacle detection facility attaches the current

robot pose in an absolute working coordinate system.

In later sense cycles older obstacle lists are re-

trieved from the FIFO queue in order to be examined

for obstacle representatives which might now be in

the blind angle of the laser scanners. Note that in this

context the algorithm drops the abstraction of obsta-

cles in favour of simplicity. This does not result in a

loss of information as all the robot can actually do if

it detects a remembered obstacle at its ﬂank is try to

keep away from it by adding a translational compo-

nent to the steering command. Which representative

belongs to what obstacle is in that sense irrelevant.

To ﬁll the obstacle memory routine Fill-

ObstacleMemory (See Listing 3) examines all ob-

stacles in a provided obstacle list. In order to deter-

mine the relation between the vehicle at its current

pose and the scan points registered earlier, all rep-

resentatives are ﬁrst of all transferred into the abso-

lute working coordinate system of the robot using the

In the following this list shall be referred to as obstacle

list.

Figure 5: Schema of how to determine whether an obstacle

representative resides in the blind region of the vehicle.

pose previously attached to the obstacle list (Subrou-

tine ToAbsoluteContext). Intuitively explained

this is as if you would virtually drive a robot model

through an absolute landscape while checking for

points in the blind regions of the robot. That way it is

not necessary to transform the representatives when-

ever the robot changes its pose.

Listing 3: Fill the Obstacle Memory.

F i l lO b s ta c l e Me m o ry ( s h o r t t e r m m e m o r y l e f t ,

s h o r t t e r m m e m o r y r i g h t ,

o b s t a c l e l i s t ,

c u r r e n t r o b o t p o s e )

f o r a l l ( o b s t a c l e i n o b s t a c l e l i s t ) do

f o r a l l ( r e p r e s e n t a t i v e i n o b s t a c l e ) do

a b s o l u t e r e p r e s e n t a t i v e =

T o A bs o l u te C o n te x t ( P o se ( o b s t a c l e l i s t ) ,

r e p r e s e n t a t i v e )

y = P o i n t O nL in e ( XAxis ( c u r r e n t r o b o t p o s e ) ,

a b s o l u t e r e p r e s e n t a t i v e )

x = P o i n t O nL in e ( YAxis ( c u r r e n t r o b o t p o s e ) ,

a b s o l u t e r e p r e s e n t a t i v e )

i f ( Abs ( y ) < Width ( s h or t t e r m m e mo r y ) / 2 AND

Abs ( x ) < Len g t h ( s h o rt

t e r m m em o r y ) / 2 ) {

i f ( T oRig h t OfRob o t ( c u r r e n t r o b o t p o s e ,

a b s o l u t e r e p r e s e n t a t i v e ) )

Append ( s h o r t t e r m m e m o r y r i g h t ,

a b s o l u t e r e p r e s e n t a t i v e )

e l s e

Append ( s h o r t t e r m m e m o r y l e f t ,

a b s o l u t e r e p r e s e n t a t i v e )

f i

y r o me M e lc a t s bO l l iF

Once in an absolute context the coordinates

can reside where they are. With the subroutine

OBSTACLE DETECTION IN MOBILE OUTDOOR ROBOTS - A Short-term Memory for the Mobile Outdoor Platform

RAVON

145

PointOnLine the distance of the representative to

the X-axis and the Y-axis of the robot-local coordi-

nate system is computed (See Figure 5). This yields

the robot-local coordinate of the representative which

offers a straightforward check whether the represen-

tative resides in a blind area of the robot. Depend-

ing whether the representative is rather to the left or

the right of the robot it is added to the corresponding

short-term memory. Routine Trim (See Listing 4)

works in analogy just the other way around to sweep

representatives from the short-term memories which

are no longer in the blind angle of the scanners.

Listing 4: Trim the Short-term Memory.

Trim ( s h o r t t e r m m e m o r y , c u r r e n t r o b o t p o s e )

f o r a l l ( r e p r e s e n t a t i v e i n s h o rt te r m m e m or y ) do

y = P o i n t O nL in e ( XAxis ( c u r r e n t r o b o t p o s e ) ,

r e p r e s e n t a t i v e )

x = P o i n t O nL in e ( YAxis ( c u r r e n t r o b o t p o s e ) ,

r e p r e s e n t a t i v e )

# Check w h e t her t h e r e p r e s e n t a t i v e i s

# s t i l l i n t h e b l i n d a n g l e

i f ( Abs ( y ) > Width ( s h or t t e r m m e mo r y ) / 2 OR

Abs ( x ) > Len g t h ( s h o rt te r m m e m or y ) / 2 )

Remove ( r e p r e s e n t a t i v e , s h o rt te r m m e m or y )

f i

mirT

Note that the robot pose used during the proce-

dures outlined above currently relies on odometry

only. This is due to the fact that odometry is locally

coherent in comparison to data fused from odometry,

GPS, the inertial measurement unit and the magnetic

ﬁeld sensor. Eventhough globally more accurate, the

latter three introduce certain local jumps into the pose,

which are a problem for precise local navigation. The

pose drift immanent in odometry on the other hand is

not an issue for the approach presented in this paper

as it is enough to be rather precise over the distance

of a vehicle length. When the robot has passed the

obstacle it is erased from the short-term memory any-

way. In the future visual odometry shall additionally

be called into service in order to gain more accurate

pose information. In particular on slippery surfaces

the authors expect this combination to be a lot more

robust than banking on odometry only.

5 EVALUATION IN

EXPERIMENTS

The short-term memory described before was im-

plemented and integrated into the control system of

Figure 6: Image of the test scenario with overlaid trace.

RAVON. In order to evaluate the approach several test

runs have been executed. First of all autonomous nav-

igation along predeﬁned tracks has been executed. In

this case the vehicle follows the given direction to the

target point as long as there is no hindrance. In case of

obstacles, however, the straight motion is interrupted

by avoidance behaviours. This effect remains active

even if the obstacle is in the blind side region. There-

fore the robot gets back on track only as soon as all

hindrances are left behind.

For clariﬁcation of the properties of the short-term

memory in this section a test run is presented where

the robot is remote controlled. In this experiment the

robot is driven through a locally ﬂat environment. All

the time the behaviour-based control system evaluates

sensor information and overrides the given commands

if necessary in order to avoid collisions.

The scenario for this run is hilly grass land as

demonstrated in Figure 6. The image shows some

trees to be avoided as well as some bushes with un-

known load bearing surface. For clariﬁcation the ap-

proximate path of the vehicle is overlaid. Four mark-

ers indicate positions referred to in the following de-

scription.

Using the localisation system the trace of the ve-

hicle can be plotted as presented in Figure 7. Some of

the trees and bushes are integrated in order to point

out the sensor data and vehicle reaction. Figure 8

shows the scanner data as well as clusters and sector

values (see Section 4.1) for the four positions indi-

cated by markers in the trace plot. Additionally data

of the obstacle memory at the side of the vehicle is

depicted as blue lines.

Position 1 shows a situation where the robot was

steered into the direction of a tree. After an evad-

ing manoeuvre the robot is now located next to the

tree. The sensor data shows that the obstacle is at

the edge of the side region and would eventually be

hidden. However, the blue lines indicate data stored

in the obstacle memory. Therefore the robot contin-

ues preserving a minimal distance to the obstacle. At

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146

Figure 7: Top view position diagram of the experiment.

position 2 the robot is in a similar situation. A tree

which was detected by the laser range ﬁnder at the

front changed its relative position such that it is lo-

cated at the right side of the robot.

Positions 3 and 4 show the situation where the

robot is manually steered to the left. Due to the ob-

stacles detected and stored in the short-term memory

the evading behaviours force the robot away from the

hindrances. This interaction results in a smooth fol-

lowing of the bushes.

The experiments showed a major improvement of

the reaction to obstacles. Behaviours regarding the

short-term memory prevent the robot of driving into

the direction of hidden obstacles. For static obstacles

this leads to a system reacting on hindrances which

can be located anywhere around the vehicle.

6 CONCLUSIONS AND FUTURE

WORK

In this paper we presented an approach for virtually

extending the coverage of obstacle detection sensor

systems. The formulated algorithm uses information

about the vehicle motion to propagate sensor data into

the blind zone of the robot. Due to a massive reduc-

tion of stored data the system features low memory

usage. Additionally there is no need for calculating

the correspondence between consecutive data making

the approach applicable for realtime scenarios.

Next steps involve the integration of a stereo cam-

Figure 8: Sensor data visualisation during the experiment.

OBSTACLE DETECTION IN MOBILE OUTDOOR ROBOTS - A Short-term Memory for the Mobile Outdoor Platform

RAVON

147

era system. Due to the device’s limited ﬁeld of view

the presented algorithm leads to an eminent extension

of the supervised area surrounding the vehicle. Addi-

tionally the accuracy of the local positioning system

will be enhanced by visual ego motion estimation.

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