GMDH Application for autonomous mobile robot’s control system construction

A.V. Tyryshkin, A.A. Andrakhanov, A.A. Orlov

Tomsk State University of Control Systems and Radioelectronics

E-mail:E-mail: rim1282@mail.rurim1282@mail.ru

Classification of existing autonomous robots

Nearest analog – agricultural AMR “Lukas”

Basic works on GMDH application to AMR control

C.L. Philip Chen, A.D. McAulay Robot Kinematics Learning Computations Using Polynomial Neural Networks, 1991;

C.L. Philip Chen, A.D. McAulayRobot Kinematics Computations Using GMDH Learning Strategy, 1991;

F. Ahmed, C.L. Philip ChenAn Efficient Obstacle Avoidance Scheme in Mobile Robot Path Planning using Polynomial Neural Networks, 1993;

C.L. Philip Chen, F. AhmedPolynomial Neural Networks Based Mobile Robot Path Planning, 1993;

A.F. Foka, P.E. TrahaniasPredictive Autonomous Robot Navigation, 2002;

T. Kobayashi, K. Onji, J. Imae, G. Zhai Nonliner Control for Autonomous Underwater Vehicles Using Group Method of Data Handling, 2007;

Part I Inductive approach to

construction of AMR control systems

Problems of AMR design

Navigation Obstacle Recognition Autonomous Energy Supply Optimal Final Elements Control Technical State Diagnostics Objectives Execution Knowledge Gathering and Adaptation

Generalized structure of AMR

Objective aspects of AMR control system construction

Utility

Realizability

Appropriateness

Classification

Taking into account Internal system parameters

Forecasting

Features of AMR obstacle recognition

Lack of objects’ a priori information Objects to recognize are complex ill-conditioned

systems with fuzzy characteristics Objects are characterized by high amount of

difficultly- measurable parameters

It is necessary to take into account internal systems parameters for objects’ classification according to “obstacle/not obstacle” property, i.e. it isn’t possible to find out is this object obstacle or not without regard for system state.

There is no necessity to perform full object identification, i.e. it isn’t necessary to answer a question “What object is this?”

Part IIAutonomous Cranberry

Harvester

Expected Engineering-and-economical Performance

Nominal Average AMR speed:

Cranberry harvesting coverage:

Relative density of harvested cranberry:

Total weight of harvested cranberry per season:

Season income:

kgdaysdayh

hkg 1440003010480season

4608002.3144000 kgUSDkg$$ USD

hkg

mkg

hmPrel 4801.04800 22

hmmh

mSharvest2

48002.14000

hkm

nom 4

Automated cranberry harvester

Part IIISimulation Results

Object Recognition Data Sample

Learning samples – 92; Training samples – 50.

Values’ Ranges:

Object Length L Є [0;20] м;

Object Width w Є [0;20] м;

Object Height h Є [0;20] м;

Recognizing Modified Polynomial Neural Network

31

32

231

232

32

31

41

24

224

224

31

22

222

222

32

13

14

213

214

14

13

24

12

14

212

214

14

12

22

227514

227513

2212

3386.36605.00329.05832.12282.33182.1

5629.143526.0296.70054.03085.0290.6

7788.62438.00244.30060.02089.07575.2

631.208304.50856.73786.00954.7692.11

054.260944.79610.78830.1213.11805.14

0500.00003.00003.010500.2104863.99573.0

0700.00004.00033.010157.210569.47673.0

0855.00707.00062.00034.00071.07512.0

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hthtthF

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Objective Functions’ Data Sample

Learning samples – 140; Training samples – 140.

Values’ Ranges:

Surface density of cranberry distribution ρcranberry Є [0;1] kg/m2;

Cranberry harvesting efficiency η Є [20;75] %;

Average AMR speed Vaverage Є [0;7] km/h;

Nominal average AMR speed Vnomaverage Є [2;4] km/h;

AMR engine fuel consumption per 100 km Pfuel Є [150;600] liters/100 km.

Values’ laws of variation:

Objective Functions

22 012.086.11057.0, tVtVVtmF averageaveragecranberryaveragecranberrycranberry

tVVtmF averagecranberryaveragecranberrycranberry 223 693.077.1110684.6,

fuel

cranberryfuel

averagecranberryfuelcranberry PPVmmF

11257

14.3710874.4, 223

Function of maximal cranberry harvest in preset time:

Function of maximal cranberry harvest in minimal time:

Function of maximal cranberry harvest with minimal fuel consumption:

Main Indices of Simulation Data

CRPercentage of

Errors

0.055 12%

F(mcranberry,Δt) F(mcranberry,t) F(mcranberry, mfuel)

CR BS CR BS CR BS

3.8e-4 9.8e-3 8.6e-3 0.9 1.8e-3 1.6

1) Obstacle recognition criterion values

2) Objective Functions criterion values

“Man should grant a maximal freedom to the computing machinery. Like a horseman having lost a way leave it to a discretion of his horse...”

A.G. Ivakhnenko. “Long-term forecasting and complex system control”, Publ. “Технiка”, Kiev, 1975. – p. 8.

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Область объектов-препятствий

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Современные состояние разработок в области АПК

Итерационный алгоритм МГУА с разделением обучения