gmdh application for autonomous mobile robot’s control system construction a.v. tyryshkin, a.a....
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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: [email protected]@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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LtLttLF
hthtthF
whwhhwF
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.
Нахождение разделяющих областей в пространстве параметров распознавания
Пространство параметровраспознавания
Область объектов-непрепятствий
Область условнопреодолимых препятствий
Область объектов-препятствий
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Современные состояние разработок в области АПК
Итерационный алгоритм МГУА с разделением обучения