GMDH Application for autonomous mobile robots control system construction

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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
2) Objective Functions criterion values
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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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