Mobile Robot Navigation Search Algorithm

7/27/2019 Mobile Robot Navigation Search Algorithm

1/14

Int J Adv Manuf Technol (2011) 52:763775

DOI 10.1007/s00170-010-2749-5

ORIGINAL ARTICLE

A new mobile robot navigation using a turning pointsearching algorithm with the considerationof obstacle avoidance

Jinpyo Hong Kyihwan Park

Received: 12 August 2009 / Accepted: 25 May 2010 / Published online: 12 June 2010 Springer-Verlag London Limited 2010

Abstract When a robot goes from the initial position to

the goal position in an unknown environment, we needthe autonomous navigation for avoiding the obstaclesand moving toward the goal position simultaneously.

Among the various methods, we focus on the naviga-

tion method by using the geometric analysis with a laserscanner having the high resolution. When the robot

turns around the obstacle, our proposed navigation

method supplies the robot with the turning point foravoiding the obstacle and moving on the shortest path.

At the same time, the next heading velocity is generated

for the robot to have the maximum velocity by using the

distance between the current position of the robot and

the turning point. The robot executes the navigationin the unknown workspace which the various obstacles

are randomly located. As the experimental results, weobtain the shortest path of the robot regardless of the

obstacles shape in the unknown environment.

Keywords Mobile robot Navigation Turning point

searching algorithm Obstacle avoidance

1 Introduction

An autonomous navigation means the method that the

robot avoids the obstacle and control the heading angle

J. Hong K. Park (B)Dept. of Mechatronics, Gwangju Institute of Scienceand Technology, 1, Oryongdong, Bukgu, Gwangu,Republic of Koreae-mail: [email protected]

J. Honge-mail: [email protected]

of the robot at the same time. For navigating effectively

in the unknown environment, the robot should considerthe obstacle avoidance, the path generation for reach-ing the goal position, and the velocity profile.

The autonomous navigation has been researched

into the velocity space method and the geometry spacemethod. The velocity space methods (Dynamic window

approach [4], Curvature velocity technique [11, 13]) are

the method which determines the maximum velocityof the robot considering the kinematic constraints and

the dynamic constraints. The robot obtains the high ve-

locity but the local minima problem is frequently hap-

pened in these methods. On the contrary, the geometry

space methods (Artificial potential field [7, 9, 14], Ago-raphilic algorithm [5], Vector field histogram [2, 17],

Nearness diagram [10], Vertical edge detection algo-rithm [1]) are the method that gives the robot the

heading angle for avoiding the obstacle by using the

geometric relation such as the force field of the ob-stacle, the polar histogram of the obstacle distribution,

and the situated-activity paradigm. We obtain the safe

path as trajectories of the robot in the geometry space

methods. However, these methods do not make theshortest path such as Dubins curve [3] because they

lack the distance information of the obstacles. In the

previous research of Yang et al. [16], fuzzy. Brait-enberg navigation scheme using the closeness of the

robot to an obstacle has been proposed. This method

is based on the distribution directives of the range

sensors on the robot. Considering the directives ofthe range sensors, we seldom obtain the shortest path

because the total path toward the goal position is not

considered.Since we obtain the scanning data of the obstacles

precisely with a laser scanner, we consider a navigation
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


2/14

764 Int J Adv Manuf Technol (2011) 52:763775

method by using the geometric analysis with the laser

scanning data in order to generate the shortest path all

the time.The navigation method is the method whichthe robot searches for a turning point of the obsta-

cle when the obstacle is located in the straight path

between the robot and the goal position. The turning

point is the point around which the robot turns for

avoiding the obstacles. A shortest path can be deter-mined as the path having the tangential direction to

the circle located on the turning point. In addition,a velocity profile of the robot can be determined by

using the distances among the robot position, the goal

position, and the turning point. Therefore, it is impor-

tant to determine the turning point in our navigationmethod. Our navigation paradigm is so simple but it is

a suitable and effective navigation method for using the

laser scanner since it has strength points of the velocityspace method and the geometry space method.

In this paper, we propose the new navigation method

by using the turning point searching algorithm withthe laser scanning data and supplying the robot with

the velocity profile. For guaranteeing the motion of the

robot in the shortest path to goal position, we design the

stable position controller by using the Lyapunov func-tion. With our developed mobile platform(GIMOR),

we perform the experiments for verifying the perfor-

mance of our proposed navigation in the unknowncomplex environment that the various shape obstacles

including the u-shape obstacle are located randomly.

Finally, we compare the our proposed method withthe curvature velocity technique and the vector field

histogram by presenting experimental results including

the trajectory and spanned time.

2 The basic principle of the navigation algorithm

Lets suppose that we know the initial position, piand the goal position, pg. The current position, pc is

obtained by using the local sensing technique, deadreckoning [6].

When there is no obstacle on the way to the goal

position, the straight motion is how to move to the goalposition in the shortest path. If the width of the straightpath to the goal position is larger than the diameter

of the robot, the robot can go straight to the goal

position. However, when the robot encounters withthe obstacles located in the straight path between the

current robot position and the goal position as shown

in Fig. 1, the robot should turn around the obstacle. In

this case, our navigation strategy is how to search fora turning point of the obstacle around which the robot

turns. For securing the safety of the robot turning, the

robot turns around the corner whose radius is twice

the radius of the robot defined as the dangerous regioncircle(DRC). Since the path1 is shorter than the path2,

we determine the path1 as time optimal path. Then,

the robot moves toward the tangential direction to theDRC from the current position of the robot. Therefore,

it is most important to search for the turning point in

our navigation framework.All of the robot motion trajectories can be rep-

resented by the combination of turning motion and

straight motion. When the robot moves toward thegoal position, the robot turns around the obstacle and

then makes a straight drive toward the goal position.

On the other hand, when the robot is entrapped by

the obstacles, the robot faces with the local minima

Fig. 1 Framework of thenavigation
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


3/14

Int J Adv Manuf Technol (2011) 52:763775 765

problem. In the later section, we will explain the turning

point searching algorithm for generating the locally

shortest path in the turning motion and escaping thelocal minima problem.

3 Turning point searching algorithm

3.1 Turning motion

3.1.1 Selection of the free segment and the temporary

turning point

The following are the three steps for selecting the free

segment and the temporary turning point.

1. Step A1: Figure 2 shows the laser scanning data

distributed on the surface of the obstacle usingsolid dot points. The scanning points on the surface

of obstacles are called group data. The scanningline with no sampled data is called free segment.Using the iterative endpoint fit method [18] , we

obtain the corner points of the obstacles indicated

by using white dot points as shown in Fig. 2.

2. Step A2: Lets define the two endpoints of the freesegment as (pfl)i and (pfr)i, where i is the number

of the free segments. We can find out minimum

value among the distances pc(pfl)i + (pfl)ipg andpc(pfr)i + (pfr)ipg. If (pfl)m is the point which givesthe minimum distance, then we can select the free

segment which have (pfl)m.

3. Step A3: And then we judge whether a distance of

the free segment is larger than the robot diameter.

If the condition is satisfied, (pfl)m is determinedas a temporary turning point. Otherwise, we re-

peat step A2 with i-1 free segments except for theselected free segment. And then we can find out

the temporary turning point through step A3. In

Fig. 2, the left endpoint of the free segment 4 is

selected as the temporary turning point, ptt becausethe pc(pfl)4 + (pfl)4pg is the shortest path.

3.1.2 Examination of the robot entrance criteria

When we obtain the temporary turning point, the ex-

amination of the robot entrance criteria are performed

in two steps.

1. Step B1: We divide the scan region into the left

and right regions based on the reference line, pcpttas shown in Fig. 3. In the left and right regions,we only use the corner points in order to reduce

Fig. 2 Selection of the freesegment
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


4/14


Fig. 3 Division of the scanregion

the computation time instead of all the sampled

points. We easily judge whether the passage is wideenough for the robot to go through by compar-

ing the robot diameter with all of the distances

between the corner points across pcptt as shownin Fig. 4a. When the corner points are located as

shown in Fig. 4b, it is required to calculate the

distance between the segment p1p2 and the corner

point p0. When the minimum distance is wider thanthe robot diameter, we can say that the path is

safe enough for the robot to go through without

collision. If the condition fails, we should repeatsteps A2 and A3 for finding out the free segment

and the temporary turning point.

2. Step B2: Since step B1 is just the robot entrance

test in turning area, it is required again to checkthat the robot can go straight to the turning point

for the time optimal control. The straight path is

determined to the tangential direction to the DRClocated on the ptt as shown in Fig. 5. However,

when the robot moves to the temporary turning Fig. 4 Minimum distance
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


5/14


point, it can collide with the corner point because

of the robot size. To prevent this condition, we

investigate the distances between the straight pathto the temporary corner point and other corner

points whose distance from pc is smaller than pcpttwhich is obtained as follows.

Pc Ptt =(Pc,x Ptt,x)2 + (Pc,y Ptt,y)2 (1)

When there is a distance smaller than the radius

of the DRC, RD, we take the corner point as a new

temporary turning point. As shown in Fig. 5, since thedistance from pri is smaller than RD, the new temporary

turning point is changed into the pri. And then, step B2

is repeated as the same way for the new temporary turn-ing point. As shown in Fig. 6, since all of the distances

between the corner points and the new straight path

are larger than RD, we determine the new temporaryturning point pri as the turning point pt.

3.2 Local minima problem

When the robot is entrapped with the obstacles orthere is no passage for the robot to go through, the

turning point is determined by comparing the heuristic

distances of pcple + plepg and pcpre + prepg, where pleand pre are the left end point and the right end pointof the scanning data. Since pcple + plepg is shorter thanpcpre + prepg as shown in Fig. 7a, ple is determined as

the turning point. When the robot rotates left and then

it is located as shown in Fig. 7b, pre is determined as the

turning point because pcpre + prepg is shorter than theother.

Likewise, since the turning point is repeatedlychanged into Ple or Pre according to the pose of the

robot, the local minima problem that the robot cannot

go out the u shape obstacle happened. Therefore, We

use a weight method of turning the direction to solvethe local minima problem. The weight method means:

1. When the robot is faced with a local minima prob-

lem, there is no passage for the robot to go through,

the robot determines the turning point.

2. At that time, the robot reserves the determinedturning direction and it goes toward the deter-

mined turning point.

3. When the robot is again faced with the local min-ima problem, the robot determines the turning

Fig. 5 Examination on thepath criterion
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


6/14


Fig. 6 Determination of theturning point

point considering the reserved turning direction.

For example, if the robot reserves the left turning

direction when the local minima is happeningfirstly, the robot determines the left endpoint as

the turning point for having the left turning di-rection. Finally, the robot can escape the local

minima.

4 Mobile robot control

4.1 Control scheme

When the obstacle is in the way to pg as shown in Fig. 8,

the robot turns around the corner points of the obstacle.Since the corner point 4 is selected as pt by using the

Fig. 7 Local minima problem
http://-/?-http://-/?-


7/14


Fig. 8 Current position, nextvia position and positionerror

Obstacle

np

c

npxcx

cy

npy

3e 1e2e

GX

GY

LX

LY

tx

ty

Goal Position

cp

npp

tp

1 2

3 4

Path1

gp

ip

Initial Position

turning point searching algorithm, the next via position,pnv is determined as a position that lies on the tangential

path to the DRC located on pt

for avoiding the obstacle.

Since the mobile robot is controlled always to reach pnvconsidered as a sub goal position, our control scheme

generates the shortest path, path1 as shown in Fig. 8.

The distance error vector E = (e1, e2, e3)t between

the next via position vector pnv and the current positionvector pc is expressed as

E =

e1e2

e3

= T pnv pc

=

cos c sin c 0

sin c cos c 00 0 1

pnv pc

(2)

where, T is the transformation matrix which converts

the global coordinate position error to the local coordi-

nate position error.

4.2 Mobile robot controller

For robot navigation, it is required to determine the ro-

bots next via position, pnv and the next heading velocity

Vnv. A robot control block diagram is represented byusing the robot kinematics T, position controller f,

motor controller C, and plant of the mobile robot.Vnv and pnv are the input commands. d is the distur-bance. J is the Jacobian Matrix of the differential drive

robot.

When the robot moves ideally for the motor input,

position feedback control is not required. However,since the robot encounters with the wheel slip, un-

certainties of the motor and robot dynamics, and the

disturbance d in Fig. 9, position control is required. Thereal velocity command V is determined by the position

error vector E and Vnv.

If we assume that the motor rotates perfectly by

using velocity control in the inner control loop, thevelocity of the mobile robot Vc = (vc, c)t can be equal

to the position controller output V = (v, )t. Then, theerror dynamics can be obtained by differentiating the

position error defined in Eq. 2 as follows.

e1 = e2 v + vnv cos e3

e2 = e1 + vnv sin e3

e3 = nv (3)
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


8/14


Fig. 9 Control block diagram

In order to stabilize the system described by Eq. 3,

the control law v and are selected as follows.

v = k1e1 + vnv cos e3

= nv +

k2vnve2 + vnv(k3e|e3| + k4) sin e3

(4)

where k1, k2, k3 and k4 are positive constants. Substitut-

ing Eq. 4 into Eq. 3, we have the following closed looperror dynamics.

e1 = e2 k1e1

e2 = e1 + vnv sin e3

e3 =

k2vnve2 + vnv(k3e|e

3|

+ k4) sin e3

(5)

To show the asymptotical stability of the closed loop

error dynamics, we define the unbounded Lyapunov

function candidate:

V=1

2

e2

1+ e2

2

+

1 cos e3

k2(6)

which has its time derivative along its motion trajectory

as

V= e1e1 + e2e2 +sin e3

k2e3

= e1e2 k1e21 e1e2 + vnve2 sin e3

sin e3

k2

k2vnve2 + vnv

k3e

|e3| + k4

sin e3

= k1e2

1+ vnve2 sin e3 vnve2 sin e3

1

k2vnv

k3e

|e3| + k4

sin2 e3

= k1e2

1

1

k2vnv

k3e

|e3| + k4

sin2 e3 0 (7)

Therefore, we can say that our proposed controller is

always stable in tracking the next heading via position

vector. It will be addressed how the next heading veloc-ity Vnv = (vnv, nv)

t are obtained in Section 5.

5 Next heading velocity generation method

5.1 Next heading linear velocity

When there is no obstacle in the path on the way tothe goal position from the initial position, the robot

speed profile has the shape of the trapezoidal shape

as shown in Fig. 10a, having an acceleration range,

a constant maximum speed range, and a decelerationrange. This velocity profile gives the time optimal track-

ing control of the robot. When the robot is located in

an arbitrary position all over the region, the velocityprofile is represented as the velocity field as shown in

Fig. 10b. The distance between pc and pi, denoted asdci, and the distance between pc and pg, denoted asdcg are selected as the variables of the next headinglinear velocity function which is represented using the

exponential curve as

vn = Vmax

2

1 + KieKadci+

2

1 + KgeKddcg

3

(8)

where Vmax, Ki, and Kg are the maximum velocity,

initial velocity coefficient, and goal velocity coefficient,respectively. Ka and Kd are determined so that the next

Fig. 10 a Next linear velocity

profile with the obstacleunconsidered. b Next linearvelocity field with theobstacle unconsidered

Velocity

Position

maxV

(a) (b)

2D

R

cid

cgd

ip

gp

cp

nv

ip

gp y position

x position

Normalizedlinearvelocity

1

0.8

0.6

0.4

0.2

1500

10001000

1500

500 5000 0

0
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


9/14


Fig. 11 a Next linear velocityprofile with the obstacleconsidered. b Next linearvelocity field with theobstacle considered

heading linear velocity has the maximum velocity when

both dci and dcg are larger than the radius of the DRCas shown in Fig. 10b.

When the obstacle exists on the way to the goal

position, the robot should be controlled to have its

velocity decreased as the it approaches the obstacle asshown in Fig. 11a. When the robot is located in the

turning point, it has Vmax velocity. The velocity is

again increased to the zero velocity as the robot movesaway from the obstacle. When the robot is located in an

arbitrary position all over the region, the next heading

linear velocity field with the obstacle considered isrepresented as shown in Fig. 11b. The velocity function

is represented as

vo = Vmax

2

1+ KtpeKdcdct 1

(9)

where, Ktp is the velocity coefficient of the turning

point and Kdc is the deceleration coefficient. dct is the

distance between pc and ptp.

Finally, we generate the overall next heading linearvelocity field with the obstacle considered by summing

Eqs. 8 and 9 as vnv = vn + vo.

The next heading via position should be determinedfor the mobile robot to have the maximum veloc-

ity around the obstacle(path1) as shown in Fig. 12a.

However, when the robot turns far around the obsta-

cle(path2), it moves still with the maximum velocityfrom the velocity profile as shown in Fig. 12b. When

the robot happens to enter inside the DRC (path3),

it moves with the reduced velocity determined from

the next heading velocity profile. Therefore, the robotcan always move safely to the goal position with the

maximum velocity profile.

5.2 Next heading angular velocity

Now, a next heading angular velocity, nv should also

be determined as the reference input for the positioncontroller. When the mobile robot is located outside of

the DRC, the next heading angular velocity is zero since

the straight motion occurs.When the robot is in the DRC, the curvilinear mo-

tion occurs having the relation ofnv = vnv/Rnv wherethe radius of curvature in the curvilinear motion, Rnv is

equal to the radius of the DRC, RD.

6 Experiment results

The system parameters of our mobile platform, GI-

MOR are shown in the Table 1. The experiments are

Fig. 12 a Combined nextlinear velocity field.b Combined next linearvelocity profile
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


10/14


Table 1 System parameters Control Unit Next heading Unit Robot Unit

parameters velocity gains parameters

k1 1 Vmax 0.33 m/s Total mass 60 kg

k2 0.3 Ki 0.538 Height 0.8 m

k3 0.1 Ka 7.97 Width 0.5 m

k4 0.4 Kg 0.7 Wheel 0.24 m

diameter

Kd 7.97 RD 0.4 m

Ktp 1Kdc 9.21

performed in a 10 8 m workspace. We used the laser

scanner, SICK LMS 200 made by the German com-pany. The performance of the laser scanner depends on

the reflectivity of the obstacles. The reflectivity gener-

ally is changed by the surface of the obstacle, the color,and the material. The reflectivity values of the repre-

sentative materials are listed in the Table 2. We per-

formed the experiments about how much reflectivity isrequired to utilize our proposed navigation algorithm.In Fig. 13, the relationship between the reflectivity and

the range is shown when the laser scanner has the

completed spot. As the range is longer and longer, theobstacles having the more reflectivity are needed. Since

the black color rarely reflects the laser spot, this kind

of colors surface is difficult to apply to our navigation

algorithm in the range of 20 m. We need the obstacleshaving the reflectivity more than 10% cardboard, matt

black. Actually, we utilized the obstacles which was

made of paper, plastic, wood, and steel. The colors of

used obstacles are brown and white. The surfaces of theobstacles are flat but the obstacles with many pieces are

randomly located on the workspace.The initial position and the goal position of the robot

are known in this environment. The experiments are

performed for the cases of the different goal positions

in which the robot starts from the same initial positionas shown in Fig. 14a, b. The obstacles are located on

the workspace as shown in Fig. 14e captured at the snap

Table 2 Reflectivity values

Material Reflectivity (%)

Cardboard, matt black 10

Cardboard, grey 20

Wood (raw pine, dirty) 40

PVC, grey 50

Paper, matt white 80

Aluminum, anodised, black 110150

Steel, rust-free shiny 120150

Steel, very shiny 140150

Reflectors >2,000

shot location in Fig. 14a. As shown in Fig. 14a,b, the big

circle means the DRC located on the turning point. Therobot always goes toward the tangential direction to the

DRC. As a result, we obtain the trajectory of the robot

represented by the small circle array. For the differentgoal positions, the robot actively reacts to the obstacle

environments and moves always on the shortest path

toward the goal position. The velocities of the robot forthe navigation are shown in Fig. 14c, d. The velocity ofthe robot almost sustains the maximum velocity gen-

erated by the next heading velocity function because

the robot keeps the shortest path determined by ourframework. The second experiments are performed

when the robot is entrapped with the U shaped obsta-

cles. The initial position of the robot is located under

the local minima situation and the goal positions aregiven respectively as shown in Fig. 15a, b. The obstacles

are located randomly as shown in Fig. 15e. As shown in

Fig. 15a, b, the robot escapes the local minima situation

captured with the obstacles. Although the robot faceswith the U shaped obstacles in the way to the goal

5 10 15 20 25 30 350

5

10

15

20

25

Range [m]

Required

reflectivity[%]

Fig. 13 Relationship between reflectivity and range with goodvisibility
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


11/14


Fig. 14 Navigationexperiment 1: a Robottrajectory and map of theobstacle. b Velocity profilewhen the initial position andthe goal position are (300,700 cm) and (900, 400 cm).c Robot trajectory and mapof the obstacle. d Velocity

profile when the initialposition and the goal positionare (300, 700 cm) and (900,450 cm). e Workspace

position, the robot easily turns around the U shaped

obstacle by using the turning point. As a result, weobtain the trajectory composed of the shortest paths. In

the second experiments, the velocities of the robot are

presented in Fig. 15c, d. The robot keeps the maximumvelocity all the way until the robot reaches the goal

position in the second experiments as well as the first

experiments. For comparing the performance of the

navigation algorithms, we executed the experiments 1and 2 by using the curvature velocity technique and the

vector field histogram under the same environments

including the initial position and the goal position ofthe robot. The performance indices of the navigation

algorithms are summarized in the Tables 3 and 4, re-

spectively. The trajectory in our proposed navigation

method is shorter than any other algorithms and our

proposed algorithm requires the shortest spanned time

regardless of any environments. Our proposed methodcan be applicable and efficient for public service robots

instead of the human for collecting the merchandize in

the market or for guaranteeing the safety in the officeat night because the fast motion of the robot is required

in those situations.

7 Conclusion

In this paper, the new navigation method has been pro-

posed using the turning point and the next heading ve-

locities in the unknown environment. The advantage ofour proposed method is that the robot always can move

on the locally shortest path regardless of the shape of
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


12/14


Fig. 15 Navigationexperiment 2: a Robottrajectory and map of theobstacle. b Velocity profilewhen the initial position andthe goal position are (450,760 cm) and (950, 900 cm).c Robot trajectory and mapof the obstacle. d Velocity

profile when the initialposition and the goal positionare (450, 760 cm) and (950,400 cm). e Workspace

Table 3 Navigationalgorithm performancecomparison for Experiments1a and b

Algorithms Experiment 1(a) Experiment 1(b)

Trajectory Spanned Trajectory Spanned

(m) time (s) (m) time (s)

Our proposed method 8 25.5 7.5 23.4

Curvature velocity technique 8.5 27 8.1 25

Vector field histogram 8.2 27.3 7.9 25.4

Table 4 Navigationalgorithm performancecomparison for Experiments2a and b

Algorithms Experiment 2(a) Experiment 2(b)

Trajectory Spanned Trajectory Spanned

(m) time (s) (m) time (s)

Our proposed method 11 34.6 11.4 35.8

Curvature velocity technique 12.2 36.5 12.6 38.2

Vector field histogram 12.0 36.8 11.8 38.7


13/14


the obstacles and the change of the goal position in the

unknown environment. In addition, since the robot has

the possible maximum velocity considered the turningpoint, the robot can reach the goal position fast and

safely. Our navigation by using the geometry analysis is

the suitable navigation reflecting the strength points of

the laser scanner with the higher distance and angular

resolutions than other sensors used frequently in themobile robot. The main contribution of this method is

that the geometry analysis navigation using the turningpoint is an effective and robust navigation applicable in

the unknown environment including all shapes obsta-

cles. Meanwhile, since our proposed method considers

the static obstacle, it can not guarantee the complete-ness of the autonomous navigation in the workspace in-

cluding dynamic obstacle [8]. In the future, our method

will be improved for the robot to move on the shortestpath in the dynamic environment. In addition, all of the

navigation method including our navigation method

have the limitation that they can not guarantee globalconvergence to the goal position because they use a

local environment data in the unknown environment

[12, 15]. For solving this problem, it is necessary to

develop the global convergence algorithm to avoid theglobal trap situations.

References

1. Borenstein J, Koren Y (1988) Obstacle avoidance with ul-trasonic sensors. IEEE J Robot Autom 4(2):213218. doi:

10.1109/56.20852. Borenstein J, Borenstein J, Koren Y (1991) The vector field

histogram-fast obstacle avoidance for mobile robots. IEEETrans Robot Autom 7(3):278288. doi:10.1109/70.88137

3. Dubins LE (1957) On curves of minimal length with a con-straints on average curvature. Am J Math 79:497516

4. Fox D, Burgard W, Thrun S (1997) The dynamic windowapproach to collision avoidance. IEEE Robot Autom Mag4(1):2333. doi:10.1109/100.580977

5. Ibrahim M, McFetridge L (2001) The agoraphilic algorithm:a new optimistic approach for mobile robot navigation. In:Proc IEEE/ASME international conference on advanced in-telligent mechatronics, vol 2, pp 13341339, vol 2. doi:10.1109/AIM.2001.936923

6. Ibrahim M, Ibrahim M, Fernandes A (2004) Study on mo-

bile robot navigation techniques. In: Fernandes A (ed)

Proc IEEE international conference on industrial technol-ogy IEEE ICIT 04, vol 1, pp 230236, vol 1. doi:10.1109/ICIT.2004.1490288

7. Koren Y, Borenstein J (1991) Potential field methods andtheir inherent limitations for mobile robot navigation. In:Proc IEEE international conference on robotics and automa-tion, pp 13981404. doi:10.1109/ROBOT.1991.131810

8. Kruusmaa M (2003) Global level path planning for mo-bile robots in dynamic environments. J Intell Robotics Syst38(1):5583. doi:10.1023/A:1026296011183

9. Li Y, He K (2005) A novel obstacle avoidance and naviga-tion method of outdoor mobile robot. In: Proc th interna-tional conference on advanced robotics ICAR 05, pp 653656. doi:10.1109/ICAR.2005.1507478

10. Minguez J, Montano L (2000) Nearness diagram naviga-tion (nd): a new real time collision avoidance approach.In: Proc IEEE/RSJ international conference on intelligentrobots and systems (IROS 2000), vol 3, pp 20942100.doi:10.1109/IROS.2000.895280

11. Minguez J, Montano L (2009) Extending collision avoidancemethods to consider the vehicle shape, kinematics, and dy-namics of a mobile robot. IEEE Trans Robot 25(2):367381.doi:10.1109/TRO.2009.2011526

12. Minguez J, Montano L, Simeon T, Alami R (2001) Globalnearness diagram navigation (gnd). In: Proc ICRA roboticsand automation IEEE international conference on, vol 1,pp 3339. doi:10.1109/ROBOT.2001.932526

13. Minguez J, Montano L, Santos-Victor J (2002) Reac-tive navigation for non-holonomic robots using the ego-kinematic space. In: Proc IEEE international conference onrobotics and automation ICRA 02, vol 3, pp 30743080.doi:10.1109/ROBOT.2002.1013699

14. Seki H, Shibayama S, Kamiya Y, Hikizu M (2008) Prac-tical obstacle avoidance using potential field for a non-holonmic mobile robot with rectangular body. In: ProcIEEE international conference on emerging technologies andfactory automation ETFA 2008, pp 326332. doi:10.1109/ETFA.2008.4638414

15. Ulrich I, Borenstein J (2000) Vfh local obstacle avoidancewith look-ahead verification. In: Proc IEEE internationalconference on robotics and automation ICRA 00, vol 3,pp 25052511. doi:10.1109/ROBOT.2000.846405

16. Yang X, Patel RV, Moallem M (2006) A fuzzybraitenbergnavigation strategy for differential drive mobile robots.J Intell Robotics Syst 47(2):101124. doi:10.1007/s10846-006-9055-3

17. You B, Qiu J, Li D (2008) A novel obstacle avoidancemethod for low-cost household mobile robot. In: Proc IEEEinternational conference on automation and logistics ICAL2008, pp 111116. doi:10.1109/ICAL.2008.4636130

18. Zhang L, Ghosh BK (2000) Line segment basedmap buildingand localization using 2d laser rangefinder. In: Proc IEEEinternational conference on robotics and automation ICRA

00, vol 3, pp 25382543. doi:10.1109/ROBOT.2000.846410
http://-/?-http://-/?-http://-/?-http://dx.doi.org/10.1109/56.2085http://dx.doi.org/10.1109/70.88137http://dx.doi.org/10.1109/70.88137http://dx.doi.org/10.1109/100.580977http://dx.doi.org/10.1109/100.580977http://dx.doi.org/10.1109/AIM.2001.936923http://dx.doi.org/10.1109/AIM.2001.936923http://dx.doi.org/10.1109/AIM.2001.936923http://dx.doi.org/10.1109/ICIT.2004.1490288http://dx.doi.org/10.1109/ICIT.2004.1490288http://dx.doi.org/10.1109/ICIT.2004.1490288http://dx.doi.org/10.1109/ROBOT.1991.131810http://dx.doi.org/10.1109/ROBOT.1991.131810http://dx.doi.org/http://dx.doi.org/10.1023/A:1026296011183http://dx.doi.org/http://dx.doi.org/10.1023/A:1026296011183http://dx.doi.org/10.1109/ICAR.2005.1507478http://dx.doi.org/10.1109/ICAR.2005.1507478http://dx.doi.org/10.1109/IROS.2000.895280http://dx.doi.org/10.1109/IROS.2000.895280http://dx.doi.org/10.1109/TRO.2009.2011526http://dx.doi.org/10.1109/TRO.2009.2011526http://dx.doi.org/10.1109/ROBOT.2001.932526http://dx.doi.org/10.1109/ROBOT.2001.932526http://dx.doi.org/10.1109/ROBOT.2002.1013699http://dx.doi.org/10.1109/ROBOT.2002.1013699http://dx.doi.org/10.1109/ETFA.2008.4638414http://dx.doi.org/10.1109/ETFA.2008.4638414http://dx.doi.org/10.1109/ETFA.2008.4638414http://dx.doi.org/10.1109/ROBOT.2000.846405http://dx.doi.org/10.1109/ROBOT.2000.846405http://dx.doi.org/http://dx.doi.org/10.1007/s10846-006-9055-3http://dx.doi.org/http://dx.doi.org/10.1007/s10846-006-9055-3http://dx.doi.org/http://dx.doi.org/10.1007/s10846-006-9055-3http://dx.doi.org/10.1109/ICAL.2008.4636130http://dx.doi.org/10.1109/ROBOT.2000.846410http://dx.doi.org/10.1109/ROBOT.2000.846410http://dx.doi.org/10.1109/ROBOT.2000.846410http://dx.doi.org/10.1109/ICAL.2008.4636130http://dx.doi.org/http://dx.doi.org/10.1007/s10846-006-9055-3http://dx.doi.org/http://dx.doi.org/10.1007/s10846-006-9055-3http://dx.doi.org/10.1109/ROBOT.2000.846405http://dx.doi.org/10.1109/ETFA.2008.4638414http://dx.doi.org/10.1109/ETFA.2008.4638414http://dx.doi.org/10.1109/ROBOT.2002.1013699http://dx.doi.org/10.1109/ROBOT.2001.932526http://dx.doi.org/10.1109/TRO.2009.2011526http://dx.doi.org/10.1109/IROS.2000.895280http://dx.doi.org/10.1109/ICAR.2005.1507478http://dx.doi.org/http://dx.doi.org/10.1023/A:1026296011183http://dx.doi.org/10.1109/ROBOT.1991.131810http://dx.doi.org/10.1109/ICIT.2004.1490288http://dx.doi.org/10.1109/ICIT.2004.1490288http://dx.doi.org/10.1109/AIM.2001.936923http://dx.doi.org/10.1109/AIM.2001.936923http://dx.doi.org/10.1109/100.580977http://dx.doi.org/10.1109/70.88137http://dx.doi.org/10.1109/56.2085http://-/?-http://-/?-http://-/?-


14/14

Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Science

& Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv

without the copyright holder's express written permission. However, users may print, download, or email

articles for individual use.

Mobile Robot Navigation Search Algorithm

Documents

Transcript of Mobile Robot Navigation Search Algorithm