Andrakhanov_AA

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: rim1282@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. McAulay
Robot Kinematics Computations Using GMDH Learning Strategy, 1991;

F. Ahmed, C.L. Philip Chen
An Efficient Obstacle Avoidance Scheme in Mobile Robot Path Planning using
Polynomial Neural Networks, 1993;

C.L. Philip Chen, F. Ahmed
Polynomial Neural Networks Based Mobile Robot Path Planning, 1993;

A.F. Foka, P.E. Trahanias
Predictive 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

Classification

Realizability

Taking into account
Internal system
parameters

Appropriateness

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 difficultlymeasurable 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 II
Autonomous Cranberry
Harvester
Expected Engineering-and-economical Performance

Nominal Average AMR speed:
 nom  4 km h



Cranberry harvesting coverage:
2
m
m
Sharvest  4000
 1.2m  4800
h
h
Relative density of harvested cranberry:
2
m
Prel  4800
 0.1kg 2  480 kg
h
h
m
Total weight of harvested cranberry per season:
season

kg
 480
h
 10 h
day
 30days  144000 kg
Season income:
$  144000 kg  3.2USD
kg
 460800 USD
Automated cranberry harvester
Part III
Simulation 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
F21  0.7512  0.0071  w  h  0.0034  h 2  0.0062  w 2  0.0707  h  0.0855  w
F31  0.7673  4.569 10 5  h  t  2.157 10 7  t 2  0.0033  h 2  0.0004  t  0.0700  h
F41  0.9573  9.4863 10 5  L  t  2.500 10 7  t 2  0.0003  L2  0.0003  t  0.0500  L
   7.9610  F   7.0944  F  26.054  F
 11 .692  7.0954  F  F  0.3786  F   7.0856  F   5.8304  F  20 .631  F
 2.7575  0.2089  F  L  0.0060  L  3.0244  F   0.2438  L  6.7788  F
 6.290  0.3085  F  L  0.0054  L  7.296  F   0.3526  L  14 .5629  F
 1.3182  3.2282  F  F  1.5832  F   0.0329  F   0.6605  F  3.3386  F
F22  14 .805  11 .213  F21  F41  1.8830  F41
F42
F23
F13
F14
1
3
2
3
2
1
4
3 2
2
1
2
1
4
2 2
2
2 2
4
2
2
4
1 2
2
1 2
3
1 2
4
1
4
2
2
3
1
2
1
3
2
2
2
4
3 2
1
3
2
3
1
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
Function of maximal cranberry harvest in preset time:




F mcranberry , t  0.057  cranberry 2 Vaverage   11.86 cranberryVaverage   t  0.012Vaverage   t 2
Function of maximal cranberry harvest in minimal time:




F mcranberry , t  6.684103  2  11.77  cranberryVaverage   0.693 cranberry Vaverage 2  t
Function of maximal cranberry harvest with minimal fuel consumption:




F mcranberry , m fuel  4.874103  cranberry 2  37.4  Vaverage 2 
1
Pfuel
 1257 cranberry 
1
Pfuel
Main Indices of Simulation Data
1) Obstacle recognition criterion values
CR
Percentage of
Errors
0.055
12%
2) Objective Functions criterion values
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
“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.
Нахождение разделяющих областей в пространстве параметров
распознавания
Пространство параметров
распознавания
Область объектов-непрепятствий
h
L

`
Область условно
преодолимых препятствий
Область объектов-препятствий
Современные состояние разработок в области АПК
Итерационный алгоритм МГУА с разделением обучения