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GMDH Application for autonomous mobile robot’s control system construction

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.

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GMDH Application for autonomous mobile robot’s control system construction

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  1. 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

  2. Classification of existing autonomous robots

  3. Nearest analog – agricultural AMR“Lukas”

  4. 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;

  5. Part I Inductive approach to construction of AMR control systems

  6. Problems of AMR design • Navigation • Obstacle Recognition • Autonomous Energy Supply • Optimal Final Elements Control • Technical State Diagnostics • Objectives Execution • Knowledge Gathering and Adaptation

  7. Generalized structure of AMR

  8. Objective aspects of AMR control system construction • Utility • Realizability • Appropriateness • Classification • Taking into account Internal system parameters • Forecasting

  9. Features of AMR obstacle recognition • Lack of objects’ a priori information • Objects to recognize are complex ill-conditioned systems with fuzzy characteristics • Objects are characterizedby 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?”

  10. Part IIAutonomous Cranberry Harvester

  11. $ ExpectedEngineering-and-economical Performance • Nominal Average AMR speed: • Cranberry harvesting coverage: • Relative density of harvested cranberry: • Total weight of harvested cranberry per season: • Season income:

  12. Automated cranberry harvester

  13. Part IIISimulation Results

  14. 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 Heighth Є [0;20] м;

  15. Recognizing Modified Polynomial Neural Network

  16. 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 efficiencyη 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 kmPfuel Є [150;600] liters/100 km. Values’ laws of variation:

  17. 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:

  18. Main Indices of Simulation Data 1) Obstacle recognition criterion values 2) Objective Functions criterion values

  19. “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.

  20. Thank you for attention!

  21. Нахождение разделяющих областей в пространстве параметров распознавания

  22. Современные состояние разработок в области АПК

  23. Итерационный алгоритм МГУА с разделением обучения

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