USING PASSAGES TO SUPPORT OFF-ROAD ROBOT NAVIGATION

Christopher Armbrust, Helge Sch

afer and Karsten Berns

Robotics Research Lab, Department of Computer Sciences

University of Kaiserslautern

P.O. Box 3049, 67653 Kaiserslautern, Germany

Keywords:

Virtual sensors, Virtual sensor probes, Passages, Off-road navigation, Autonomy.

Abstract:

In this paper an approach for the detection of passages and their use in autonomous off-road robot navigation

is presented. The authors argue that many two-layered architectures of robot navigation systems suffer from

the gap between the typically coarse-grained high-level path-planning and the basically reactive low-level

collision avoidance. In this context, passages shall be deﬁned as paths leading through obstacles. The proposed

approach is based on the idea that passages in the proximity to the robot should be evaluated with respect to

their relevance for reaching the target area in order to avoid local detours by following suitable passages.

The detection and assessment of passages is based on virtual sensors, a standardized data representation of-

fering a uniﬁed, straightforward, and ﬂexible retrieval mechanism for accessing the data provided by different

sensor systems. For the evaluation of passages the authors introduce the concept of virtual sensor probes which

can move independently from the robot. That way the point of view on the environment information can be

tailored to support the detection and evaluation strategy.

The proposed approach was deployed on the mobile off-road platform RAVON which serves as a testbed for

the experiments carried out in the context of this work.

1 INTRODUCTION

Many robot navigation systems are designed follow-

ing a two-layer concept: A deliberative path-planner,

the “navigator”, builds global maps, which it uses for

high-level path planning. The output of this planner

are single path points, which are passed to a lower

level component, the “pilot”. This component trans-

forms the target coordinates into motion commands,

taking into account sensor data to realize collision

avoidance. The pilot typically operates in a nearly

reactive way, storing only little state information and

having a very much limited view on the robot’s en-

vironment. In such an architecture, the pilot exerts

a draw towards the target, which the anti-collision

system may counteract, resulting in the robot driving

around obstacles towards the target.

While this principle works well in simple envi-

ronments, it can easily fail in more complex ter-

rain. As the collision-avoidance works locally, miss-

ing the “big picture”, it cannot keep the navigator

from drawing the robot away from a path and into

small openings between obstacles, which are so wide

Figure 1: The problem of dealing with indentations.

that the collision avoidance does not get active di-

rectly, but which could be easily recognized as inden-

tations when using a larger scope (see Figure 1, (1)).

The idea that is presented here is to search for

paths leading through obstacles, so-called passages,

and to evaluate them with respect to their value for the

robot’s navigation. In the described situation, the path

the robot is driving on would be detected as such a

189

Armbrust C., Schäfer H. and Berns K. (2009).

USING PASSAGES TO SUPPORT OFF-ROAD ROBOT NAVIGATION.

In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 189-194

DOI: 10.5220/0002198501890194

 SciTePress

passage, while the indentation would be ignored (see

Figure 1, (2)). Furthermore, estimating a passage’s

orientation would allow for directing the robot in a

way that it can more easily enter the passage.

It shall be emphasized here that the described ap-

proach targets at environments in which there is no

clear path that could be followed using a path track-

ing approach like the ones mentioned in section 2. In-

stead, the paths constituted by passages are consecu-

tive spaces between obstacles that are wide enough to

be used by a robot.

2 STATE OF THE ART

The concept of separating a robot navigation system

into two layers is widespread and described in many

places in the literature. The classic separation results

in a low-level system for local navigation that has a

limited view on the world and operates in a fast, yet

shortsighted manner. Different approaches have been

followed to establish the interaction of the two parts

((Wooden et al., 2007), (Ranganathan and Koenig,

2003)). An approach for detecting narrow passages in

indoor environments is described in (Schr

oter, 2005).

Its author used the polar data of a laser scanner to

identify and assess passages and combined it with 3D

rectangular objects reconstructed from the images of a

stereo vision system to identify doors. The algorithm

used for processing the laser data resembles the one

presented here. However, it only processes the data

of one polar sensor and not of several (virtual) sen-

sors. Evidently, using a stereo system in such a way to

detect passages will not work in unstructured off-road

environments. Approaches for keeping the robot from

leaving the road or path include detecting curbs using

a light-stripe scanner (Thorpe et al., 2003), detecting

lanes using edge extraction from images, or road de-

tection using a combination of LADAR data and color

information (Hong et al., 2002). The work described

in (Lieb et al., 2005) uses the assumption that the ve-

hicle is situated on the road to form templates of the

road’s appearance and from them and current images

calculates an estimate of the road’s curvature. (Alon

et al., 2006) describes a system that uses two different

path-ﬁnding algorithms in parallel and uses the output

of the one with the highest conﬁdence. However, all

of these approaches are tailored to detecting a path in

an environment that is completely different from the

one of this work.

3 SYSTEM ARCHITECTURE

3.1 A Two-layered Navigation System

The work at hand is embedded into a two-layered

robot navigation system consisting of a classical

deliberative navigator (Braun and Berns, 2008) on

top of a behavior-based system for local navigation,

i.e. target-oriented motion and collision avoidance

(Sch

afer et al., 2008b). The navigator creates and up-

dates topological maps of the environment, which are

used as basis for the path planning process. It sends

the points of a path one by one to the lower layer, from

which it receives basic status information.

3.2 Virtual Sensors

Special behaviors of the local navigation system

translate the goal poses into motion commands, which

are altered by a behaviour-based anti-collision sys-

tem. The behaviors use a special type of representa-

tion that provides a powerful yet slim interface to ar-

bitrary types of sensors. Different aspects and ranges

of the robot’s environment are abstracted using po-

lar and Cartesian sector maps, which represent virtual

sensors (VS). Polar maps are deﬁned by start and stop

angles whereas Cartesian maps have an extent in pos-

itive and negative y-direction. Each sector holds the

most relevant representative of the area it covers. In

the architecture presented here, the VS are ﬁlled with

data from a scrollable, robot-local, and orientation-

ﬁxed grid map that stores highly preprocessed sensor

data of a pannable laser range ﬁnder (Sch

afer et al.,

2008a). Using this short-range map as input for the

virtual sensors allows for monitoring areas which are

currently not in the range of the robot’s real sensors.

3.3 Virtual Sensor Probes

While the virtual sensors used for collision avoid-

ance are deﬁned with respect to the robot coordinate

system (RCS) and are ﬁxedly mounted on the robot,

virtual sensors that are used to gather information

about speciﬁc structures in the environment have to

be ﬁx with respect to the working coordinate system

(WCS). Hence their pose in terms of the RCS changes

whenever the robot moves. This special type of vir-

tual sensor is called virtual sensor probe (VSP).

The preprocessing of the sensor data, the coordi-

nate transformations (sensor to robot to working CS),

and the ﬁlling of the VSPs with the correct data is

done in the sensor processing subsystem. Therefore

higher components do not have to deal with these

technical details, but can simply provide a WCS pose

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

190

Figure 2: The views of a real sensor and a virtual sensor

probe.

for a VSP and be assured that it is ﬁlled with the cor-

rect information.

Using VSPs, the robot can “look” at a place in

the environment from a different point of view (see

Figure 2). While this naturally does not yield new

information that the robot cannot gather from its ac-

tual pose, it structures the existing sensor data in an

abstract way, allowing for the use of more straightfor-

ward algorithms.

4 THE USE OF PASSAGES

4.1 Passage Detection

A passage is deﬁned by its entry, which is delimited

by an obstacle on each side. These two obstacles are

called Passage Entry Points (PEP), the line connect-

ing them Passage Entry Line (PEL). The algorithm

for detecting passages traverses all sectors s

of a po-

lar sector map (see Figure 3, (1)) covering the area in

front of the robot and compares the distances d

of the

obstacles o

(with 0 ≤ i < n and n being the number

of sectors).

If o

j+1

is farther away from the origin than o

at least the threshold t

, then o

is considered as po-

tential ﬁrst PEP. Be o

with k ≥ j +1 the ﬁrst obstacle

that is closer to the origin than o

j+1

by at least the

threshold t

. Then o

is the second PEP and all ob-

stacles between o

and o

are considered to lie within

the passage. The new passage is added to a list and

the search goes on with the remaining sectors. In the

following, pseudocode for the detection algorithm is

shown.

For every passage, the passage entry midpoint

(PEMP), which is deﬁned as the point lying in the

middle between o

and o

, is calculated. It is a pas-

sage’s characteristic point and, as described below, is

important for navigating into a passage.

As the distance values in the sectors refer to

the virtual sensor’s coordinate system, the algorithm

Algorithm 1. Detecting Passages.

searching for = cFIRST PEP; // the status of the search process

index ﬁrst pep = 0; // the ﬁrst PEP’s index

index point in passage = 0; // the index of a point within the passage

for (i = 0; i < n - 1; i++) do

if (searching for = cFIRST PEP) then

if (d[i] - d[index ﬁrst pep] < t 0) then

index ﬁrst pep = i;

else

searching for = cSECOND PEP; index point in passage = i;

else // currently searching for second PEP

if (d[index point in passage] - d[i] >= t 1) then

passages.add(new Passage(o[index ﬁrst pep], o[i]);

index ﬁrst pep = i;

searching for = cFIRST PEP;

else

// nothing to be done here

Figure 3: The sector maps used for passage detection.

tends to not detect passages that lie in the sideway

parts of the polar sector map. To overcome this de-

ﬁciency, two Cartesian sector maps are also used as

data sources (see Figure 3, (2)). Due to the generic

interface of the sector maps, the algorithm described

above needs only simple modiﬁcations to process the

data of Cartesian sector maps.

It shall be remarked here that by using sector

maps, the passage detection and evaluation algo-

rithms do not have to access the complex grid map,

but can operate on a much simpler data structure.

A temporal aspect was integrated by creating and

updating a list of persistent passages: In every sen-

sor processing cycle, the PEMP of each passage that

is detected is compared to the PEMP of each persis-

tent passage. Two passages are regarded as similar if

the distance between their PEMPs is below a certain

threshold. Taking into account the passages’ PEPs

was also considered, but experiments showed that the

distance between two PEMPs is a suitable criterion

for similarity. If a similar persistent passage is found,

it is updated with the newly found passage, i.e. the

persistent passage’s PEPs and PEMP are set to the

ones of the new passage. If not, the newly found pas-

sage is added to the list of persistent passage. Per-

sistent passages that have not been seen for a certain

amount of time are removed.

USING PASSAGES TO SUPPORT OFF-ROAD ROBOT NAVIGATION

191

4.2 Passage Evaluation

The passage evaluation operates on the persistent pas-

sages. Its ﬁrst step checks whether a passage fulﬁlls

the basic requirements, i.e. whether it is wide enough,

has a minimum age, lies in a suitable direction, has

a suitable orientation, and is long enough. A pas-

sage which fulﬁlls these requirements is called suit-

able passage.

The ﬁrst four aspects can be checked easily. A

passage’s width is calculated as the distance between

its two PEPs. If a passage’s entry is too narrow for the

robot, it is obviously irrelevant for navigation. The

age is calculated as time that has passed between the

ﬁrst and the latest detection. Passages which have not

been seen for a minimum amount of time are ignored.

As passages shall be used to assist the navigation

system in driving the robot to its target, a suitable pas-

sage shall lie in the direction of the target with respect

to the robot. This is checked by comparing the ori-

entation of two lines—one going from the robot to

the target and one going from the robot to the PEMP.

Furthermore, a suitable passage has to point approxi-

mately in the direction of the target. This is checked

by ﬁrst calculating the orientation of a line that starts

at the PEMP and is perpendicular to the PEL. This ori-

entation is then compared to the one of a line starting

at the PEMP and going to the target. If the difference

between the two orientations is below a certain thresh-

old, the passage is considered to be well-oriented.

A VSP represented by a Cartesian sector map con-

sisting of only one sector is used to “look” into the

passage, measure the distance to the closest obstacle

and thus estimate the passage’s length (see Figure 4,

(1)). Using a sector map as VSP instead of accessing

the grid map directly facilitates the data access and

spares the evaluation component more complex data

processing.

Figure 4: The sector maps for length and orientation esti-

mation.

It is advisable to navigate the robot in a way that

it reaches the PEMP with approximately the passage’s

orientation as this facilitates entering the passage. The

orientation estimation described above does not take

into account information about the presence of obsta-

cles in the passage. Thus a more precise estimate is

calculated for the passage that best satisﬁes the above

criteria, the relevant passage. Two Cartesian sector

maps are used to monitor the sides of the passage (see

Figure 4, (2)). Each of their sectors stores informa-

tion about the closest obstacle in the area it covers, i.e.

it contains a local estimate of how far away the pas-

sage’s border is. For each sector, the angle between

the following two lines is calculated: (a) a line that

starts at the PEMP, is perpendicular to the PEL and

points into the passage, and (b) a line going from the

PEMP to the sector’s obstacle. The passage’s orien-

tation is calculated from the arithmetic mean of these

angles, so its axis is pushed away from obstacles (see

Figure 4, (3)). Of course this is only a rough esti-

mate, but it can be calculated using the existing mech-

anisms, while other methods need more complex, spe-

cialized algorithms.

4.3 Integration

The component that detects and evaluates passages

and navigates the robot to a PEMP has been imple-

mented as behavior and integrated into the behavior-

based system mentioned above. It is referred to as

passage behavior. Whenever it detects a relevant pas-

sage, it gets active and sends the coordinates of the

PEMP to the behaviors that drive the robot to given

target coordinates. These behaviors are also used by

the navigator, but when a passage is detected, the be-

havior gets active and overwrites the target pose pro-

vided by the navigator.

In some cases, an inhibition of the passage behav-

ior is sensible. If the robot follows a wide path that

leads to the target and has only few obstacles, the pas-

sage behavior would constantly detect a relevant pas-

sage and would try to guide the robot to its PEMP. If

the number of visible obstacles is too low, the orienta-

tion calculations would produce poor estimates. Us-

ing these passages would result in a swinging robot

motion although it would be possible to drive straight

ahead. To cope with this problem, another behavior

uses a wide Cartesian sector map to monitor the area

in the direction of the target. If there is no obstacle,

there is no need for using passages, so the monitor-

ing behavior inhibits the passage behavior and thus

returns control to the navigator.

The system’s operation shall be illustrated taking

up the example from section 1. At ﬁrst, only a small

part of the situation is visible to the robot (see Fig-

ure 5, (1)). A large passage is detected between the

outer obstacles. The area in the direction of the target

(green square) is free, so the passage is ignored. As

soon as the free space to the right of the robot is rec-

ognized as indentation, the navigation system guides

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

192

the robot towards the newly detected suitable passage

instead of driving into the indentation (see Figures 5,

(2) and (3)).

Figure 5: The operation of the passage behavior in the situ-

ation depicted in Figure 1.

5 EXPERIMENT AND RESULTS

For experiments the passage detection mechanism

presented in this paper was integrated into the nav-

igation software of the mobile off-road platform

RAVON

, a 4WD vehicle with the dimensions and

weight of a city car, which is equipped with a variety

of sensor systems (e.g. 2D laser scanners, a 3D laser

scanner, stereo camera systems). Detailed informa-

tion about the robot can be found in (Armbrust et al.,

2009).

Several test runs have been conducted in the

Palatinate forest, of which one shall be presented here

as example

. RAVON started on a trail and was given

a target location several hundred meters away. Its task

was to drive there fully autonomously. Figure 7 shows

a part of the run as pose trace. Red crosses symbolize

obstacles that were classiﬁed by the sensor process-

ing system as deﬁnitely not traversable. Vegetation

that was probably traversable when driving cautiously

is marked with green crosses. It shall be pointed out

here that the robot drove the complete route depicted

in the ﬁgure without any user action.

Figure 6: RAVON.

Figure 7: The pose trace with the

checkpoints. The unit of length is

meter.

Different passages were detected during the drive.

For lack of space, only three signiﬁcant checkpoints

Ravon: Robust Autonomous Vehicle for Off-road Nav-

igation

video showing large part of test available at:

http://rrlab.cs.uni-kl.de/robot-gallery/ravon/

shall serve as examples here. For each of them, a visu-

alization of the scrollable grid map ﬁlled with the 3D

scanner’s data is depicted (see Figure 8). The robot

is situated in the map’s center (blue square). The col-

ored circles depict various types of obstacles (red: too

high to be driven over; green: other types of obstacles

with ground contact; pink: places where the robot

could hit the ground). The direction to the target is

visualized by a red line starting at the robot’s center.

Passages are visualized with circles for the PEPs and

PEMPs, and lines for the PEL and for visualizing the

ﬁrst and second orientation estimates. For reasons of

clarity, only two types of passages are displayed: suit-

able, but not relevant ones (green), and relevant ones

(blue).

Shortly after the start, at checkpoint 0, the path

made a slight turn to the left. The relevant passage

was oriented to the left, following the path (see Fig-

ure 8, (0)). Although it did not lie exactly in the direc-

tion of the target and was oriented a bit away from it,

it was still suitable. While this was a good ﬁrst exam-

ple of the passage behavior’s operation, the combined

action of the navigator and the anti-collision system

would most likely have dragged the robot around the

obstacles to its right.

At checkpoint 1 (see Figure 8, (1)), the deviation

of the path’s direction from the direction to the target

was so large that following the navigator’s drag would

have led the robot into the underwood. The usage of

the passage along with its orientation resulted in the

robot driving a curve around the obstacles to its right.

Figure 8: The situation at the three checkpoints.

At the last checkpoint (see Figure 8, (2)), the

robot’s motion would have probably been the follow-

ing without the use of the passage: When driving to-

wards the target, the robot would have gotten so close

to the obstacles ahead of it that the collision avoid-

ance behaviors would have had to turn it to the left

or the right. Due to noisy sensor data and the lack

of a larger scope, it is possible that the robot would

have been turned to the left, thus driving straight into

the underwood. At this point, backing off maneuvers

would have to be conducted. By contrast, the detec-

tion of a suitable passage made the robot turn to the

right. It avoided the obstacles before getting too close

to them and stayed on the trail. Due to the obstacles

USING PASSAGES TO SUPPORT OFF-ROAD ROBOT NAVIGATION

193

on the left border of the trail, no suitable passage was

detected there—which is the desired behavior of the

detection component.

6 CONCLUSIONS AND FUTURE

WORKS

In this paper a concept for passage detection tai-

lored to navigation in terrain with many obstacles and

without a clear path was presented. Furthermore, a

loosely coupled interaction of the passage detection

facility with a topological navigator on the basis of

passage orientation estimation was proposed. A suit-

able passage is negotiated by continuously passing the

characteristic passage entry midpoint to a behavior-

based point approacher. That way non-holonomic

platforms—as presented in the experiments by exam-

ple of the off-road robot RAVON—can enter even nar-

row passages without unneeded maneuvering.

The presented system is capable of leading the

robot through complex terrain on the basis of a sin-

gle target location avoiding local detours where pos-

sible. As only local terrain information is available

on this layer, difﬁculties like dead ends still remain a

problem. As a next step, the passage behavior shall

provide detected passages to the navigator, which

shall store them in terms of navigation-relevant spots.

When a dead end is reached, the navigator shall tag

passages leading to the current location as dead end

entry points to avoid global detours in the future.

Furthermore, the system shall be extended so that

other types of navigation-relevant places like very

narrow ways (which require especially careful move-

ments) or crossroads (which offer several options for

the robot to proceed to the target area) are also de-

tected. Exchanging information about these places

with the higher navigation layer will support back-

tracking if following a path turns out to be unhelpful

in getting to the target.

ACKNOWLEDGEMENTS

Team RAVON thanks the following companies for

their technical and ﬁnancial support: IK elektronik,

Mayser, Hankook, MiniTec, SICK, DSM Computer,

ubner Giessen, John Deere, Optima, ITT Cannon,

MOBOTIX, and Unitek.

REFERENCES

Alon, Y., Ferencz, A., and Shashua, A. (2006). Off-road

path following using region classiﬁcation and geomet-

ric projection constraints. In Proceedings of the 2006

IEEE Computer Society Conference on Computer Vi-

sion and Pattern Recognition, volume 1, pages 689–

696, New York, NY, USA. IEEE Computer Society

(Washington, DC, USA).

Armbrust, C., Braun, T., F

ohst, T., Proetzsch, M., Renner,

A., Sch

afer, B., and Berns, K. (2009). Ravon — the

robust autonomous vehicle for off-road navigation. In

Proceedings of the IARP International Workshop on

Robotics for Risky Interventions and Environmental

Surveillance 2009 (RISE 2009), Brussels, Belgium.

IARP.

Braun, T. and Berns, K. (2008). Topological edge cost es-

timation through spatio-temporal integration of low-

level behaviour assessments. In Proceedings of

the 10th International Conference on Intelligent Au-

tonomous Systems (IAS-10), Baden Baden, Germany.

Hong, T.-H., Rasmussen, C., Chang, T., and Shneier, M.

(2002). Road detection and tracking for autonomous

mobile robots. In Proceedings of SPIE Aerosense

Conference, Orlando, FL, USA.

Lieb, D., Lookingbill, A., and Thrun, S. (2005). Adaptive

road following using self-supervised learning and re-

verse optical ﬂow. In Robotics: Science and Systems,

pages 273–280, Cambridge, Massachusetts, USA.

Ranganathan, A. and Koenig, S. (2003). A reactive robot

architecture with planning on demand. In Proceed-

ings of the 2003 IEEE/RSJ International Conference

on Intelligent Robots and Systems, pages 1462–1468,

Las Vegas, Nevada, USA.

Sch

afer, H., Hach, A., Proetzsch, M., and Berns, K.

(2008a). 3d obstacle detection and avoidance in veg-

etated off-road terrain. In IEEE International Confer-

ence on Robotics and Automation (ICRA), pages 923–

928, Pasadena, USA.

Sch

afer, H., Proetzsch, M., and Berns, K. (2008b).

Action/perception-oriented robot software design: An

application in off-road terrain. In IEEE 10th Interna-

tional Conference on Control, Automation, Robotics

and Vision (ICARCV), Hanoi, Vietnam.

Schr

oter, D. (2005). Region & Gateway Mapping: Ac-

quiring Structured and Object-Oriented Representa-

tions of Indoor Environments. PhD thesis, Institut

ur Informatik der Technischen Universit

at M

unchen,

unchen, Germany.

Thorpe, C., Carlson, J., Duggins, D., Gowdy, J., MacLach-

lan, R., Mertz, C., Suppe, A., and Wang, B. (2003).

Safe robot driving in cluttered environments. In 11th

International Symposium of Robotics Research, Siena,

Italy.

Wooden, D., Powers, M., MacKenzie, D., Balch, T., and

Egerstedt, M. (2007). Control-driven mapping and

planning. In Proceedings of the 2007 IEEE/RSJ In-

ternational Conference on Intelligent Robots and Sys-

tems, pages 3056–3061, San Diego, CA, USA.

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

194