Journal of JSAM 70(6): 76～84, 2008Research Paper

Automatic Guidance System in Real-time

Orchard Application (Part1)

-A Novel Research on Coconut Field Application Using Laser Scanner-

Oscar C. BARAWID Jr.*1, Rahman FARROKHI TEIMOURLOU*1,

Noboru NOGUCHI*2, Kazunobu ISHII*2

Abstract

The research objective was to develop an automatic guidance system in a real-time coconut field

application using a laser scanner. Seven methods were used in this research, data and information

gathering, tree row recognition using Hough transform, masking method to get the ROI (region-of-interest), simulated coconut environment, SMA (simple moving average), steering control algorithm,

and experimental test runs. This research dealt with the recognition of tree rows as a straight line,

to be followed by the robot tractor in real-time coconut field straight path navigation.

[Keywords] region-of-interest, laser scanner, Hough transform, SMA (simple moving average), coconut field

Ⅰ Introduction

Presently only a few research institutions are con-

ducting studies on orchard applications using robotics

technology. Barawid et al., (2006) conducted a related

research entitled "Development of an autonomous

navigation system using two-dimensional laser scan-

ner in an orchard application". Rovira-Mas et al., (2005)

also developed a related research entitled "The Hough

transform based vision algorithm for crop row detec-

tion method for tractor automated guidance". Kise et

al., (2005) developed a similar research for crop row

detection using stereovision based agricultural ma-

chinery entitled "A stereovision based crop row detec-

tion method for tractor automated guidance". All of

these researches focused solely on general orchard

application and row crop detection.

This paper aims to describe the automatic guidance

system in a coconut field. The coconut industry is one

of the top five net foreign exchange earners with an

income of US$ 760M per annum in the Philippines. 3.5

million farmers are directly engaged in coconut culti-

vation. Developing an autonomous navigation system

in a coconut field will help the farmers in the Philip-

pines to collect the harvested fruits, to save time and

effort in harvesting coconuts, to perform an autono-

mous weeding, and to make coconut farming to be

highly-industrialized. These are the reasons why the

coconut orchard was chosen as the subject of study.

The coconut is a member of the palm family. It

grows to 30m tall, with pinnate leaves 4-6m long.

The trees flourish in tropical climates as in the Philip-

pines. One can see these trees lining the vast shore-

lines. The large bulk of the trees however are con-

tained in plantations owned by private individuals.

Continuous cultivation is due to marketing desirabili-

ty of the tree. The coconut has long been called the"tree of life" by the Filipinos due to its

abundant uses.

Water from the coconut is known for its therapeutic

value and as a natural body coolant. Coconut meat is

used as food. Much of the harvested coconuts are

sun-dried and turned into copra (dried coconut kernels).

The oil from the copra is an important ingredient in

soap and cosmetic-making (Wikipedia The Free Ency-

clopedia, 2007).

Vehicle Robotics Laboratory (Vebots), Hokkaido

University has already developed two robot tractors

that can perform various tasks such as autonomous

weeding, autonomous spraying, autonomous cultivat-

ing, and data gathering in an autonomous navigation.

To perform these tasks, the robot tractors used RTK-

GPS (real-time kinematic global positioning system)

and an IMU (inertial measurement unit) to follow a

specific navigation map. Most of the studies about

automatic guidance systems dealt with spatial posi-

tinning-sensing systems and steering control systems

for following a predetermined path (Noguchi and

Terao, 1997; Noguchi et al., 1997).

*2 JSAM Student Member , Graduate School of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kita-Ku, Sapporo, 060-8589, Japan,

TEL 011-706-2568

*2 JSAM Member , Corresponding author, Graduate School of Agriculture, Hokkaido University, Kita-9, Nishi-9, Kita-Ku, Sapporo, 060-

8589, Japan, TEL 011-706-2568;

BARAWID Jr., FARROKHI TEIMOURLOU, NOGUCHI, ISHII:Automatic Guidance System in Real-time Orchard Application (Part1) 77

The main goal of this research is to find more appli-

cations of these robot tractors. Our laboratory came

up with the idea to apply this robot tractor in an

orchard autonomous navigation. The application of

autonomous navigation in an orchard is an ideal task

because the same operations are repeatedly performed

year after year (Barawid et al., 2006). In orchard ap-

placation, it is impossible to use the RTK-GPS due tothe presence of obstructions within the vicinity. A

global sensing method works only in open spaces

(Hakura and Yoshikazu, 2001). A Laser scanner (SICKLMS 291) was used as the navigation sensor instead of

the RTK-GPS. Study on a straight follower control

algorithm also used a laser scanner as the navigation

sensor (Abe et al., 2005).

Vebots has already developed an automatic guid-

ance system in real-time orchard application using a

laser scanner (Barawid et al., 2006). The system was

tested in two vertical rows which resembles an or-

chard. The system worked well and obtained good

results. The system only described as a general or-

chard application without discussing its specific appli-

cation. General application means that the system

can be applied in an orchard where foliage is dense

and the spaces between two trees are small thereby

making it easy for the laser scanner to recognize the

tree rows as a straight line during the autonomous

navigation. Specific application means that the system

is concentrated in a particular field which, in this

particular research, is the coconut field. Therefore,objective of this research (Part1) was to modify this

developed automatic guidance system in order to

apply or use a specific autonomous guidance in a

coconut field using 2-dimensional laser scanner as the

navigation sensor. This research was the first attempt

to describe an automatic guidance system in coconut

field application. The difference between the coconut

field and other orchards like apple, strawberry, grape,

citrus orchards, etc. is the distance between the trees

and the density of the foliage. As pointed out, a

coconut tree is very tall and its branches are concen-

trated only at the topmost point. This tree feature

makes it difficult for the laser scanner to recognize the

coconut tree rows as a straight line. The previous

developed automatic guidance system for general or-

chard application (Barawid et al., 2006) worked well in

two vertical rows that resembled an orchard, because

the distances between the trees in the experiment area

were close to each other. A masking method was used

to get the region-of-interest (ROI). This method

focused solely in the rows where the robot vehicle

autonomously navigates. Hough transform was used

to recognize the coconut tree rows as straight lines.

The sensor used to recognize the coconut tree rows

was two-dimensional laser scanner which can obtain

the distance and angle of an object.

Ⅱ Research materials and methods

1. Test equipment

The research used LMS 291 (laser measurement

system) scanner as the navigation sensor substitute to

GPS. The laser scanner is NCMS (non-contact meas-

urement system), which can scan its surrounding in

two-dimensional measurements, the object's distance

and the object's angle with respect to the direction of

transmission which is counterclockwise. Table 1

Table 1 Technical specification of the laser measurement system (LMS 219)

laser scanner

78 Journal of the Japanese Society of Agricultural Machinery Vol. 70, No. 6 (2008)

Fig. 1 Laser scanner attachment position on the

front of the robot tractor

shows the technical specification of the LMS SICK 291.

The laser scanner was attached to the front of the

robot tractor as shown in Fig. 1.

KUBOTA GL320 (24-kW) agricultural tractor was

used as the platform for this research that was

modified into a robot tractor. The robot tractor can

control steering, transmission (forward and neutral),

brake system, and three-point hitches (up and down).

For evaluation accuracy of the robot tractor in auton-

omous run, VRS-RTK-GPS (virtual reference station

real-time kinematic global positioning system) was

used as the positioning sensor to get the absolute

position of the vehicle in UTM (universal transverse

Mercator) coordinate system, and IMU (inertial meas-

urement unit) was used as the heading sensor to

obtain the heading angle of the vehicle. Figure 2

shows the schematic diagram of the robot tractor and

its research components. All sensors are connected to

the laptop PC using RS-232 cable.

2. Data gathering

In Sapporo, Japan, there is no coconut field. It is

difficult to know the actual environment of coconut

field and difficult to conduct an experiment. Actual

coconut field experiment was conducted on March 17-21

, 2006 in Aurora, Philippines. The purpose of this

experiment was to obtain data from actual environ-

went of the coconut field and to test the developed

automatic guidance system for general orchard appli-

cation (Barawid et al., 2006) to see if the laser scanner

could recognize the coconut trees as a straight line.

The vehicle was run manually between coconut trees

and obtained data necessary for modification of the

automatic guidance system for general orchard appli-

cation into specific application that was the coconut

field. The laser scanner was attached on front of the

Fig. 2 Schematic diagram of the robot tractor and

its research components

Fig. 3 Laser scanner attachment position during

the actual experiment in coconut field

vehicle with a height of 70-cm. In this experiment, the

laser scanner's scanning angle and distance range

were set to 180 deg and 80m, respectively, with an

angle resolution of 1 deg and response time of 13ms.

Figure 3 shows the laser attachment position during

the experiment in the Philippines and shows the

actual coconut field environment.

3. Recognition algorithm

The Hough transform was adopted as algorithm to

recognize the coconut tree rows as straight lines. The

Hough transform usually was used for linear or

circular detection. The main advantage of Hough

transform compared to commonly used methods like

least squared error method of fitting lines to image

data is that, even if there are group of points which

BARAWID Jr., FARROKHI TEIMOURLOU, NOGUCHI, ISHII:Automatic Guidance System in Real-time Orchard Application (Part1) 79

Fig. 4 An outline how to recognize the orchard rows

using Hough transform

varies to some extent, asking for a straight line is

possible (Bourke, 1998). Also, processing is collectively

possible even when there are two or more straight

lines in the image data. The point which has the

majority of intersection served as the line equation in

the r-θ space.

The disadvantage, on the other hand, is that in

order to plot curves (i. e. sinusoids) for every observing

point (xi,yi) in Cartesian image space to r-θ polar

Hough parameter space, the load of computation is

large. The purpose of the Hough transform algorithm

was to automatically detect the coconut tree rows as

straight lines, and provide lateral offset and heading

measurements which can be sent to the vehicle con-

troller as the target course. Detailed explanation of

Hough transform was described by Barawid, et al.,

2006 in the paper entitled "Development of an autono-

mous navigation system using a two-dimensional

laser scanner in an orchard application".

Figure 4 shows the outline of the orchard row recog-

nition using the laser scanner. In the figure, the black

points were the components of the coconut trees

scanned by the laser scanner. Using r-θ parameteriza-

tion space, a point in Cartesian space (xi,yi) corre-

sponds in the r-θ space. Eqn (1) can be used to obtain

the value of the shortest distance from the origin

(Gonzales and Wood, 1993).

r=dicos(φi-θ) (0〓i〓180, 0〓 θ〓180) (1)

In Fig. 4, r is the shortest distance between the laser

scanner and the orchard row in cm, di is the measured

distance between the obstacle and the laser scanner in

cm, φi is the relative angle of the obstacle with respect

to the laser scanner detection in deg, and θ is the

deflection angle between the orchard row and the

laser scanner in deg. The φi can be limited to any

angle range depending on the application without

losing its generality. In this research, the φi limit was

0〓i〓_180 and limit was 0〓 θ〓180.

Lateral error εl can be calcuiated by Eqn (2). Using

the data obtained by the laser scanner, lateral offset

can be evaluated and become the target run course of

the robot tractor.

(2)

In this equation, the solution was made to both

positive value and negative value. In positive case,

the distance to the right-hand side from the laser

scanner to the coconut tree row was denoted by rR in

cm and the distance to left-hand side from the laser

scanner to the coconut tree row was denoted by rL in

cm with respect to the main target course. Heading

error can be calculated using Eqn (3);

(3)

where ε is the lateral error in m, L is the look-a-head

distance in m and θ is the deflection angle in deg. The

look-a-head distance is a parameter that can be con-

trolled.

4. Masking

After analyzing the gathered data of the coconut

field environment, it was found that there were many

unnecessary data included in the laser scanner's

captured data. Examples of the unnecessary data

were the points recognized at the left and right sides

of the laser scanner. The reasons for the capture of

these unnecessary data were due to the large dis-

tances in between trees which averaged 6m, and the

broadness of the scanning angle and distance range of

the laser scanner. Figure 5 shows the data taken from

the experiment run in the Philippines. Parallel to the

robot tractor centerline, the white points and the

white lines represent the trees in the coconut field and

the tree rows recognition, respectively. In this exper-

iment, the laser scanner could not recognize the tree

rows as straight line. The black line represents the

line that the vehicle will follow in an autonomous

navigation. The laser scanner's parameter setting was

80 Journal of the Japanese Society of Agricultural Machinery Vol. 70, No. 6 (2008)

(a) Laser scanner's scanning angle and distancerange setting were 180deg and 80m, respectively

(b) Actual captured image of laser scanner incomputer window of coconut trees

Fig. 5 Laser scanner's data acquisition

180deg scanning angle, 80m distance range and an

angle resolution of 1deg.

The coconut trees were recognized as a single point

only because the scanning resolution was set to 1deg,

the lowest setting resolution of the laser scanner.

Actually, there were four angle resolutions to choose

from in the laser scanner setting 0.25, 0.50, 0.75, and 1.0

deg. It was possible to increase the angle resolution of

the laser scanner, however, the data will be too bulky

to process and will have time delay in the system. The

1deg resolution was used because it was enough to

perform tree row recognition given that a coconuttree's diameter was sufficient. The scanned points are

the edges of the trees which made it possible to recog-

nize the tree rows as a straight line which is basic

information needed in navigation.

In order to recognize the coconut trees as a straight

line, elimination of unnecessary data is required. The

ROI (region-of-interest) was made by masking method.

To determine the ROI, appropriate scanning angle of

(a) Laser scanner's scanning angle and distancerange setting;

(b) Laser scanner's actual captured image of thecoconut field after applying the maskingmethod.

Fig. 6 Laser scanner's data acquisition with masking

laser scanner must be considered in order to obtain

two vertical rows of the coconut trees fitted to the

ROI. In this method the laser scanner's hardware

scanning angle and distance range were set into 100

deg and 32m, respectively. Different scanning angle

and distance range settings were tried to determine

the scanning angle and distance range in the software

setting. The appropriate scanning angle and distance

range were determined as 60deg and 32m, respective-

ly. This masking setting could obtain the ROI with

minimal error. Figure 6 shows the masking of the

coconut filed environment. In the figure, the unneces-

sary data were removed in the captured image data.

Only the ROI was left and the laser scanner has been

able to recognize the coconut rows as straight line

with respect to the desired path. Figures 6a and 6b

show the laser scanner's area monitoring. This means

that an infringement of a field, e. g. by an object, leads

to a switching signal at an output. The laser scanner

can obtain the object's distance and angle as il-

BARAWID Jr., FARROKHI TEIMOURLOU, NOGUCHI, ISHII:

Automatic Guidance System in Real-time Orchard Application (Part1) 81

(a) Actual coconut field environment

(b) Simulated coconut field environment

Fig. 7 Actual and simulated coconut field environment

which has a tree distance and tree diameter of

approximately 6m and 30cm, respectively

lustrated in the figures.

5. Simulation of a coconut field environment

Due to its topography, coconut trees cannot grow in

Sapporo, Japan. This limitation has made experimen-

tation difficult but not impossible. In this regard, a

simulated environment of coconut field in Hokkaido

University campus was made using boxes. Thirty

pieces of fabricated trees were made to look like coco-

nut trees with an approximately same diameter with

real coconut trees which is 30cm. Figure 7 shows the

actual coconut field environment and the simulated

coconut field environment. In Fig. 7a, the tree dis-

tance is large and there are no tree branches below the

trunk of each coconut tree. The simulated coconut

field environment tree distance and tree diameter

were approximately same as the actual distance and

diameter of the coconut trees in the coconut field

which is 6m and 30cm, respectively.

6. Noise removal in the steering algorithm of the

robot tractor

The data obtained by the robot tractor PC, coming

from the laser scanner's PC have noise data. These

noise data will cause the steering to hunt/oscillate

which will, in turn, affect the accuracy of the naviga-

tion. To avoid this phenomenon, MA (moving aver-

age) was included to the steering algorithm. In this

research a SMA (simple moving average) was used.

SMA is illustrated in Eqn (4) (McQuarrie and Tsai,

1998).

(4)

where SMA is the new sequence, nd is the number of

data in each calculation, and aj is the given sequence.

Equation 4 can be also written as;

(5)

One characteristics of the SMA is that if the data has

a periodic fluctuation, then applying an SMA of that

period will eliminate that variation. In this research

nd was limited to 10 data in each calculation because in

a real-time application, large amount of data will have

a time delay in sending the data into the steering

system. The data acquisition rate was set to 1Hz.

7. Algorithm to control the steering angle of the

robot tractor

To control the steering of robot tractor, a receiving

function was developed for the robot tractor to re-

ceive the data from the laser scanner. The desired

steering angle is represented as the function of lateral

error and heading error denoted by ε and φ, respec-

tively. To solve the desired steering angle, Eqn (6) can

be used.

ωDSA=-(GainAε+GainBΔ φ) (6)

where ωDSA is the desired steering angle in deg, Δ φ is

the heading error in deg, ε is the lateral error in m,

Gain A is the lateral gain in deg/m and Gain B in deg/

deg is the heading gain. Gain A and Gain B are the

control parameters. A lookup table for L (look-a-head

distance), Gain A and Gain B was prepared according

to travel speeds of the robot tractor. The robot tractor

was run to several speeds with satisfied line-following

performance. Gain A and Gain B were determined as

0.5 and 35, respectively, which gave the minimum

error in the lookup table. The desired steering angle

was computed using these parameters (Gain A and

Gain B). This method is adaptable to guidance both

straight and curved path because the desired heading

angle is modified in each control step by updating the

target point.

82 Journal of the Japanese Society of Agricultural Machinery Vol. 70, No. 6 (2008)

Fig. 8 Illustration of the captured points of the laser

scanner transformed to sinusoids in r-θ Hough

space.

Fig. 9 Noise removal to obtain the desired steering

angle of the robot tractor

8. Experimental test runs

The developed automatic guidance system for nav-

igation in coconut field was tested in a simulated

coconut field environment. Five different speeds (0.5,

0.55, 0.64, 0.87, and 1.4m/s) were tested in the simulated

area. Using the developed system the robot tractor

could run autonomously between the simulated coco-

nut tree rows.

Ⅲ Results and discussion

1. Hough transform recognition result

Applying the Hough transform, the laser scanner

could recognize the coconut tree rows made from

boxes as a straight line. Figure 8 shows the results in

r-θ Hough space of the captured points by the laser

scanner. A cluster of intersections of sinusoids can be

seen. The majority of the intersection of these sinu-

soids that estimated the equation of the line would

become the coconut row lines. These points of major-

ity of intersections served as the solution for recogniz-

ing the coconut tree rows. The right distance of the

laser scanner from the coconut row is denoted by rR

and the left distance of the laser scanner from the

coconut row is denoted by rL.

2. Noise removal in the desired steering angle

SMA (simple moving average) was used to remove

the noise included in the data obtained from the laser

scanner's PC before sending to the tractor's PC.

Figure 9 shows the results in noise removal algorithm.

In the figure, the gray projections represent the raw

data which came from the laser scanner's PC. The

RMS (root mean squared) error of raw data without

SMA was 4deg. The black projections represent the

desired steering angle using the SMA. The RMS (root

mean squared) error for raw data with SMA was 2deg.

The SMA eliminated the big hunting in the steering

angle. Elimination of the hunting in the steering will

improve the accuracy of the system. In this research

SMA was used as the algorithm to remove the noise

because, it will not have a time delay in the system.

Minimum time delay in the guidance system is very

important in real-time application of autonomous ve-

hicle.

Noise removal in the inclination of the vehicle was

not included in this research because the experiment

field was a simulated coconut field and it was almost a

flat surface. The vehicle has an inclination error but it

was negligible due to its inability to affect the

vehicle's evaluation accuracy. But in an actual coco-

nut field, the inclination error of the vehicle is neces-

sary to be considered which will be described in the

part of the research.Also, in an actual coconut field there are many

obstacles such as weeds, grasses, plants, etc. These

obstacles were not considered in this research because

the experiment field was a simulated field only and

there were no obstacles. But in an actual coconut field

experiment these obstacles will be considered and it

will be discussed in the next part of the research.

3. Evaluating the accuracy in autonomous run

The developed automatic guidance system for coco-

nut field navigation was tested in a simulated coconut

field environment which has a width and length of 18

m and 72m, respectively. The simulated field was

composed of four rows using boxes. The robot tractor

was run autonomously between the simulated coco-

nut rows with a speed of 0.55m/s. The RTK-GPS and

IMU were used to evaluate the accuracy of the system.

Also, the centerline of the simulated coconut rows was

determined using the RTK-GPS. The purpose of ob-

taining the centerline between coconut rows was to

know if the robot tractor was following the correct

path or was running correctly at the middle of therows.

Figure 10 shows the comparison of lateral and head-

ing errors with and without the MA (moving average)

of the system. In Fig. 10a and 10b, the gray projections

showed that there were big oscillations in the lateral

BARAWID Jr., FARROKHI TEIMOURLOU, NOGUCHI, ISHII:

Automatic Guidance System in Real-time Orchard Application (Part 1) 83

(a) Comparison of lateral errors with andwithout MA (moving average)

(b) Comparison of heading errors with andwithout MA (moving average)

Fig. 10 Comparison of lateral and heading errors with

and without MA (moving average)

and heading errors. This means that it will affect the

evaluation accuracy of the system. The RMSs (root

mean squared) evaluated accuracy of the navigation

system without the MA in lateral and heading errors

were 0.138m and 8deg, respectively. The application

of MA eliminated the big oscillations in the lateral and

heading errors as shown in Fig. 10. The black projec-

tions represent the evaluated accuracy with MA.

Figure 11 shows the evaluated accuracy results of

lateral and heading errors with MA navigating in the

simulated coconut field environment. The results

showed that the robot tractor can navigate auto-

nomously in a simulated coconut field environment.

In the figure, the maximum lateral and heading errors

were 0.15m and 14deg, respectively. The lateral and

heading RMSs errors were 0.06m and 3deg, respec-

tively. The accuracy results were minimal and ade-

quate enough to run in a coconut field using thedeveloped automatic guidance system.

Test runs were conducted in a simulated coconut

field environment in different speeds in autonomous

run. The speed 0.55m/s was used to evaluate the

accuracy in coconut navigation. Table 2 shows the

results in different autonomous test runs. In the table,

(a) Lateral error of the robot tractor insimulated coconut field autonomous run

(b) Heading error of the robot tractor insimulated coconut field autonomous run

Fig. 11 Accuracy results in coconut field autonomous

navigation

Table 2 Summary of RMS (root mean squared)

lateral and heading errors in simulated

coconut autonomous run

there was no big difference in the results of lateral and

heading errors in autonomous run in different speeds.

This means that the developed system worked well

even in different speed runs.

Ⅳ Conclusion

In this research, the developed automatic guidance

system for general orchard application was modified

and developed into an automatic guidance system for

real-time coconut field application. The laser scanner

was used as the navigation sensor substitute to the

GPS (global positioning system). The laser scanner

84 Journal of the Japanese Society of Agricultural Machinery Vol. 70, No. 6 (2008)

obtained the data necessary for orchard path naviga-

tion such as the object's distance and object's angle.

An actual experiment was conducted in the Philip-

pines to obtain the data necessary for modifying thedeveloped automatic guidance system. The research

used a masking method to obtain the ROI (region-of-

interest) in the data obtained by the laser scanner.

The evaluated accuracy of the automatic guidance

system for lateral and heading errors were 0.06m and

3deg, respectively. These results were adequate enough

to navigate the robot tractor in real-time coconut field

application. The developed system was evaluated at

0.55m/s speed.

The future works of the research are (1) to test

experimental runs in an actual coconut field or appli-

cation in different orchards such as citrus, apple,

strawberry, banana, etc.; (2) to make a turning algo-

rithm for autonomous navigation in an orchard; and

(3) to develop a low cost and small scale robot vehiclethat can navigate in any given orchard application

because the vehicle platform used in this research

were expensive and quite big.

Acknowledgement

The author wishes to acknowledge the Japanese

Ministry of Education, Culture, Sports, Science, and

Technology for providing a scholarship grant to con-

duct a research in Hokkaido University, Sapporo,

Japan about the automatic guidance system using

robot tractors.

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(Received: 14 November. 2007・Question time limit: 31 January. 2009)

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コ コ ナ ッ ツ園 に お け る作 業 を想 定 して,レ ー ザ ス キ ャ

ナ を 航 法 セ ンサ と した 自動 走 行 シ ス テ ム の 開 発 を研 究 の

目的 と した。本 研 究 で は,自 動 走 行 シス テ ムを 開 発 す る上

で7つ の プ ロ セ ス が 採 用 さ れ た 。 レー ザ ー ス キ ャナ に よ

るデ ー タ取 得 ・処 理,ハ ブ変 換 に よ る樹 列 認 識 取 得 され

た距 離 空 間情 報 の マ ス キ ン グ処 理,擬 似 的 コ コ ナ ツ園 に

お け る試 験,ナ ビゲ ー シ ョ ン シ グ ナル の ノ イ ズ 除 去 の た

め の 移 動 平 均 処 理,操 舵 制 御 ア ル ゴ リズ ム お よ び 自動 走

行 試 験 で あ る。また,採 用 した ハ ブ変 換 は樹 列 を 直線 と し

て認 識 ・処 理 し,車 両 は そ の認 識 され た 直 線 を走 行 す る

よ うに操 舵 制 御 した。

[キーワー ド] (ROI) region-of-interest, 2次元 レーザスキャナ,

ハブ変換,最 小二乗法,単 純移動変換,コ コナツ園

*1 学 生会 員,北 海 道大 学大 学院 農学 研究科(〒060-8589 札 幌市北

区 北9条 西9丁 目 TEL 011-706-2568)

*2 会 員,北 海 道大 学大 学院 農学 研究 科(〒060-8589 札 幌市北 区北

9条 西9丁 目 TEL 011-706-3847)