GMDH%20Application%20for%20autonomous%20mobile%20robot

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GMDH Application for autonomous mobile robots
control system construction

• A.V. Tyryshkin, A.A. Andrakhanov, A.A. Orlov
• Tomsk State University of Control Systems and
  Radioelectronics
• E-mail rim1282_at_mail.ru

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Classification of existing autonomous robots
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Nearest analog agricultural AMR Lukas
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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

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Part I Inductive approach to construction of AMR
control systems
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Problems of AMR design

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

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Generalized structure of AMR
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Objective aspects of AMR control system
construction

• Utility
• Realizability
• Appropriateness

• Classification
• Taking into account Internal system parameters
• Forecasting

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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 isnt 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 isnt necessary to answer
  a question What object is this?

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Part IIAutonomous Cranberry Harvester
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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

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Automated cranberry harvester
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Part IIISimulation Results
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Object Recognition Data Sample

• Learning samples 92 Training samples 50.
• Values Ranges

Object Length L ? 020 ? Object Width w ?
020 ? Object Height h ? 020 ?
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Recognizing Modified Polynomial Neural Network
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Objective Functions Data Sample

• Learning samples 140 Training samples 140.
• Values Ranges
• Surface density of cranberry distribution
  ?cranberry ? 01 kg/m2
• Cranberry harvesting efficiency ? ? 2075
• Average AMR speed Vaverage ? 07 km/h
• Nominal average AMR speed Vnomaverage ? 24
  km/h
• AMR engine fuel consumption per 100 km Pfuel ?
  150600 liters/100 km.
• Values laws of variation

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Objective Functions
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
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Main Indices of Simulation Data
1) Obstacle recognition criterion values
CR Percentage of Errors
0.055 12
2) Objective Functions criterion values
F(mcranberry,?t) F(mcranberry,?t) F(mcranberry,t) F(mcranberry,t) F(mcranberry, mfuel) 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
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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.
????i??, Kiev, 1975. p. 8.
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Thank you for attention!
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