LOCATING AND CROSSING DOORS AND NARROW

PASSAGES FOR A MOBILE ROBOT

Zhiyu Xiang, Vitor M. F. Santos

Dept. of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal

Jilin Liu

Dept. of Information & Electronic Engineering, Zhejiang University, Hangzhou, 310027, P.R.China

Keywords: Mobile Robotics; Navigation; Door-crossing, Laser range finder

Abstract: In structured indoor environment, the structural information gathered from sensors can be divided into three

different levels whose features increase gradually: walls, corners and passages. Besides detecting walls and

corners, the paper focuses on narrow passage detecting and crossing. The sensor employed in the robot is a

laser range finder. By detecting the Complete Points in the laser map, two types of narrow passages are easy

to find. Two immediate applications of the proposed approach emerge: localization for robots and automatic

crossing of passages. The validity of the method is proved with experimental results.

1 INTRODUCTION

For indoor mobile robots, usually the robot has a

three-step process to navigate: sensing, processing

and driving. By defining a set of behaviors, the

information from sensors can be directly connected

with the resulting behaviors. Various methods have

been developed on sensor-based localization (Lionis,

2002) and motion planning (Chung, 1992).

The idea is to develop a feature map suitable for

behavior based navigation that can be further

integrated by a high-level language for navigation

mission specification (Santos, 2001). For most

structured indoor environments, three types of

features could be found: planar walls (short or long),

corners (convex or concave hull), narrow passages

(open or closed doors, narrow corridors). They

belong to different levels in the feature map: wall is

the basic element among all of the features; corner is

the intersection line of two or three walls; narrow

passage, which may be symbolized by corners at the

entrance, could be composed of two parallel walls

inside. With these three types of features, different

behavior of robot could be developed. Furthermore,

the features accompanied by the geometric

information could also be used for localization of

robots (Xiang, 2003). Since the narrow passages are

the highest-level features, we focus on detecting

them and consequently make use of them by

localizing and navigating the robot to pass the

narrow passage. Distinction between a door and a

simple narrow passage can be done by using

additional parameters of the algorithm such as the

width, or the “quality” of the delimiting walls. That

is not a major concern and from now on the terms

“door” or “narrow passage” will be used

interchangeably.

Several types of sensors could be used for

detecting the narrow passages. Vision is good at

object recognition (Davison, 2002), but it requires

complex processing and relies on good illumination

condition. Ultrasonic sensors are good to tell the

appearance of obstacles nearby but cannot tell the

accurate position due to their wide beams (Kulich,

1999). On the other hand, laser appears is an ideal

sensor for our purpose and it can provide accurate

2D profiles of the surrounding environments in a

mere scan. Therefore a laser scanner has been used

in the present work.

The paper is organized as follows. The second

part introduces our narrow passage-detecting

algorithm. Section 3 presents the localization of

robots by using the geometric information of

passage entrance. Implementation of behavior

“Crossing the passage” is described in Section 4.

370

Xiang Z., M. F. Santos V. and Liu J. (2004).

LOCATING AND CROSSING DOORS AND NARROW PASSAGES FOR A MOBILE ROBOT.

In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 370-374

DOI: 10.5220/0001135803700374

 SciTePress

Section 5 gives some results of our experiments and

section 6 concludes the paper.

2 DETECTION OF DOOR

2.1 Extracting Complete Points

In a laser scan, Complete Points (CP) represent

vertical corners in the real environments. As shown

in Figure 1, the ending points filled with black are

all CP because they correspond to the vertical edges

of the wall, while the others are not because their

appearance is due to obstructing of walls in front of

them.

Decision Rules of CP: For every two neighbor

line segments in the laser scan, if they are connected,

the intersection point of them must be a CP; if they

are disconnected, between two broken points, the

one that has the shorter range to the original point of

laser data is a CP.

Figure 1: Interpretation of Complete Points in laser map.

In Figure 1, point 1 is the intersection point of

two line segments, thus it is a CP. Between point 2

and point 3, point 2 has a shorter distance to the

origin of laser, meaning that point 2 is a CP while

point 3 is not.

2.2 Detecting the Entrance

Generally, in structured indoor environment, there

are two types of entrance for narrow passages,

namely, type I and type II, as shown in Figure 2. A

type I entrance consists of two corners, and type II

entrance is composed of one corner and one wall.

Whichever the type of entrance, there is at least one

corner as the basic element. Any corner in the real

environment corresponds to CP in the laser scan.

Type I

Entrance

Type II

Entrance

Figure 2: Illustration of two different types of entrances of

narrow passages.

Type I entrance detection: Here we should

check every pair of CP to see if they form a type I

entrance. Several conditions could be set up for type

I entrance detection:

• The distance from one CP to the line which the

other CP belongs to should be less than a

threshold;

• Each CP should lie on the extended part of the

line segment which the other CP belongs to and

neighbored with the other CP;

• The distance between two CP should be within

the scope of normal width of narrow passage

which robot could pass through;

• The difference of slope angle between two line

segments, which the pair of CP belongs to,

should be less than a predefined threshold.

If any CP belongs to more than one line segment,

all of the line segments it belongs to should be

checked. If there is one line segment that meets all

of the above conditions, the pair of CP form a type I

entrance of narrow passage.

Type II entrance detection: Unlike type I entrance,

type II entrance consists of one CP and one line.

Thus the conditions for forming type II entrance is

like following:

• The line to be checked should be almost

perpendicular to the line which the CP belongs

to;

• The intersection point between the line to be

checked and the line which CP belongs to

should lie on the side near the CP;

• The distance between the obtained intersection

point and the CP is within the scope of width of

entrance.

After the above two steps, each entrance has to

be checked if it is passable for the robot. Only a

passable entrance will be considered as candidate to

pass through.

LOCATING AND CROSSING DOORS AND NARROW PASSAGES FOR A MOBILE ROBOT

371

3 LOCALIZATION OF ROBOT

If the position of the detected entrance in the global

environmental map is known, it is intuitive that the

entrance could be used for localization of the robot

as a natural landmark. Given the position parameters

of the detected entrance

(, )

Gb Gb

y and (, )

Ge Ge

in the global map, and the corresponding coordinates

(, )

bLb

y and (, )

eLe

y in the local map, the

position of robot

(, ,)

in the global map could

be computed with the following expressions:

tan tan

cos sin

sin cos

Gb Ge Lb Le

RGbLb R Lb R

RGbLb RLb R

yy yy

xxx

xx x y

yy x y

θθ

−−

⎧

=−

⎪

−−

⎪

=− +

⎨

⎪

=− −

⎪

⎩

(1)

4 APPROACH AND CROSS THE

PASSAGE

To cross the passage two main steps are required:

reach the front of the entrance with the appropriate

orientation and effectively traverse the passage.

4.1 Approach the passage

Approaching the passage can be done using one of

two fundamental methods: using short-term

odometry to perform some open loop motion (few

meters only up to the door), or a closed-loop

approach based on feature tracking, such as a

Kalman filter-based technique. The later is

conceptually more robust because once the door is

located it can be continuously tracked while the

robot moves towards its center. On the other hand,

since the distances are relatively small (few meters),

short-term odometry can be fairly reliable and much

simpler to implement since simple motions are to be

carried out. Occasional dead-reckoning errors can

result in a poorer positioning near the door, which is

nonetheless not relevant since door traversing is

done in real time with continuous laser data and

corrections will occur. Nonetheless, door locating

can be done continuously and therefore door

approaching can be continuously tracked as a

posture tracking problem (de Wit et al., 1997). This

means that the desired posture near the door can be

tracked as a control problem using linear and non-

linear approaches. However, in this work, no such

tracking control was implemented since distances

are relatively small and short-term odometry clearly

satisfies the demands of the problem.

Approaching the door with open loop motion is

however not trivial; there are practical constraints

that must be taken into account. Firstly, the robot

must place itself near (in front) of the door by taking

into account its dimensions; it could simply rotate

towards that destination point (point D in Fig.4) and

move in straight line until it reaches there (using

short term odometry). This approach seems

reasonable for type I passages, but would fail for

type II since the robot could collide with the wall in

the front before reaching the front of the passage.

The solution is therefore to do the approach along a

circular arc as illustrated in Figure 3. Reporting to

Figure 3, the door location algorithm (described in

the previous sections) returns the points A(Ax, Ay)

and B(Bx,By) on the robot reference frame. The

point C(Cx, Cy) is immediately known (middle

point), and since

∆

l is defined as a parameter for the

algorithm (about 100 cm in the current approach),

D(Dx, Dy) is easily obtained taking into account, for

example, the following 3 conditions:

[CD] ⊥ [AB] , ||CD|| =

∆

l , ||OD|| is minimal (2)

R (Rx, Ry)

∆

Figure 3: Illustration of the approach to a door of type II.

Given A, B and the desired

∆l, a preliminary rotation of

angle

γ followed by an arc path completes the approach

which obviously also satisfies type I doors.

The conditions stated in (2) result in the

following expressions ready to implement

computationally for D(Dx, Dy):

(

)

xx yy

DC lAB AB=±∆ − (3)

(

)

yy xx

DC lAB AB=∆−m (4)

where

()

yy xx

AB A B A B=−+−

From the two solutions obtained for D(Dx, Dy)

the one that minimizes the norm of vector [OD], that

is, the one that is closer to the robot, should be

chosen. Knowing also that [DR] ⊥ [CD] and that D

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372

is on the circumference, it is possible to obtain the

coordinates of the arc center R(Rx, Ry):

()

22 23

xy xy yy yx y

xy yx

DCD DC DC DD D

CD CD

−+−−

−

(5)

()

2232

xxyx yx yyx

xy yx

CD CD D DD CDD

CD CD

−−−+

−+

(6)

Also, the following angles are obtained:

()

arctan ,

= ,

()

arctan ,

= and

(

)

πβδ

=− − . Finally, come the remainder

parameters for the robot motion: /2

πδ

=+ and

the length of the arc for the robot to move along

sOR

∆= ×

There are however some cases where

approaching the door using this method may not be

physically feasible due to previously unperceived

objects that become obstacles along that arc-based

path. Figure 4 (left) illustrates one such situation.

The solution is either to reject paths that fall out of

the initial covered region by the laser (initial rotation

angle γ greater that 90º) or that pass though occupied

regions and make the door unreachable, or in fist

place have a better perception system (360º laser).

Figure 4: Planned local path turns out not feasible when

further perception is available. A two-step based approach

is an alternative.

An alternative, although not so elegant solution,

is to set an additional point (E) in the line that

connects the current position of the robot the D point

extracted earlier, as shown in Figure 4 (right). The

approach is done towards this point E in a straight

line, and then the procedure can be repeated: if

distance to door is now at range, perform the arc

path calculated with the previous algorithm.

4.2 Crossing the passage

Crossing effectively the door is done with real-time

data perception and the subsequent driving of the

robot continuously in order to minimize the

difference of measurements on both areas on the

robot sides. The algorithm is conceptually simple: in

the 180º data scan it first locates apertures based on

range gradient. Then it selects the widest aperture on

its frontal region where the robot can fit (between

±60º but this is configurable) and evaluates its

relative position and orientation. Velocity motion is

generated continuously in order to drive the robot

for the middle of the free pathway. Figure 5

illustrates the main procedure

Laser range

profile

Instantaneous

direction to follow

Figure 5: Crossing the door with laser range.

The trigger that detects the end of door crossing

occurs when at least on one of the sides empty space

appears. That is, when a range of data measurements

indicates more than the normal width used for doors.

This way, the algorithm drives the robot along a

narrow passage regardless of its extension.

5 EXPERIMENTAL SETUP AND

RESULTS

The robot used is shown in Figure 6: it is a mobile

robot Robuter equipped, among other systems, with

a SICK laser rangefinder and 24 sonar sensors

distributed around the body. The laser has 180°

scanning scope with a resolution of 0.5°.

Figure 6: The experimental mobile robot: Robuter

Environment setups were built with cardboard boxes

in order to emulate more or less complex situations.

Real doors were also used but their variety is not

vast (at least 95cm wide doors were required).

Figure 7 shows one experimental set-up where two

narrow passages were constructed with one of them

blocked by some obstacles. Figure 8 shows the

corresponding laser map and detection results.

LOCATING AND CROSSING DOORS AND NARROW PASSAGES FOR A MOBILE ROBOT

373

Figure 7: Experimental setup with two narrow passages

built with paper boxes with one of them blocked ahead.

Figure 8: Laser map where two potential narrow passages

were detected but one rejected due to obstruction. The

triangle on the bottom represents the position of the robot.

In Figure 8 the dark lines represent the walls, and

the small circles at the ends of the lines represent the

Complete Points. The two detected entrances of

passages were drawn with dashed lines. The results

of this procedure are summarized in Tab.1.

Table1: Results of narrow passages detection from Figure

Type

Coordinates of

Ending Points (mm)

Width

(mm)

Passable

(T/F)

I (344, 2598) (-734, 2761) 1090 False

II (-1378, 1671) (-1171, 2807) 1154 True

Approaching doors resulted as planned and

expected since odometry demands were simple.

Both the solutions depicted in Fig. 5 and Fig. 6

turned out well with the second one more efficient in

cluttered environments.

Crossing the doors proved quite efficient with

the configuration or robot as shown in Figure 6 but a

new configuration where the laser was moved

forwards for other types of data acquisition resulted

less efficient. That is to be enhanced in the near

future.

6 CONCLUSIONS

In this paper an algorithm to detect and cross narrow

passages was described. In feature maps the passage

entrances composed of two corners or one corner

and one wall are the highest-level features and could

be regarded as important natural landmarks that

provide localization information to the robots. The

implementation of the behavior was also simple and

practical. Approaching doors and traverse them also

proved the success of the proposed methods.

ACKNOWLEDGEMENTS

This work has been developed under a bilateral

cooperation between Portugal and the People’s

Republic of China sponsored by the former ICCTI,

Ministry of Science and Technology of the

Portuguese Government.

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