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Erik Maehle

The Organic Robot Control Architecture ORCA and Its Application to the Fault-Tolerant Walking Machine OSCAR *. Erik Maehle. University of Lübeck Institute of Computer Engineering Ratzeburger Allee 160 D-23538 Lübeck E-Mail: maehle@iti.uni-luebeck.de.

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Erik Maehle

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  1. The Organic Robot Control Architecture ORCA and Its Application to the Fault-Tolerant Walking Machine OSCAR* Erik Maehle • University of Lübeck • Institute of Computer Engineering • Ratzeburger Allee 160 • D-23538 Lübeck • E-Mail: maehle@iti.uni-luebeck.de • Seminar ‚Organic Computing‘, Dagstuhl, March 31 – April 4, 2008 Supported by DFG under MA1412/7-1, associated to DFG SPP 1183 ‚Organic Computing‘ Joint Project with University of Osnabrück and Fraunhofer AIS

  2. Motivation • Autonomous mobile robots in human environments • -> unstructured, -> complex control • dynamically changing systems • environment • -> no explicit model -> no explicit fault • of the environment model • fault-tolerance, safetyengineering bottleneck

  3. Organic Systems • Organic systems adapt dynamically to environment and malfunctions • - uncertainties • - unknown environment • - unforeseen situations • Our approach: controlled self-organization • Inspiration: autonomic nervous system and immune system • Aim: - detect, react, and adapt to malfunctions • - avoid critical system states at any time • - low cost implementation and engineering

  4. Overview • Introduction • ORCA Architecture • Walking Robot OSCAR • Self-Organizing Walking • Conclusion

  5. ORCA - Organic Robot Control Architecture BCU = Basic Control Unit OCU = Organic Control Unit

  6. OCU-Architecture • - Monitor: anomaly detection • - Memory: short term history • - Reasoner: hard real-time determination of a counteraction Variant of Observer/Controller Architecture

  7. Methodological Approaches • - Signals reflecting the ´health´ of a signal or a BCU • e.g. - noisiness, confidence of output results, • - load state; error state • - Adaptive action selection • IF movement=blocked • THEN activation of commanded behaviour -=5%, • activation of non-commanded behaviour +=5% • Learning to treat malfunctions • => Learning at OCU-level

  8. OSCAR - Organic Self-Configuring and Adapting Robot • Hexapod with 18 DOF • (Servos) • Ground contact Sensor: • Simple switch per leg • Control Computer: • JControl/Smartdisplay • Servo Driver Modul • I2C-Bus • Programming Language: • JAVA

  9. Basic Approaches for Autonomic Walking Leg and Joints Stick Insect (Carausius morosus) 3 DOF Swing Phase Stance Phase Modelled by WALKNET [Cruse et al. Uni Bielefeld] AEP: Anterior Extreme Position PEP: Posterior Exreme Position

  10. Distributed Walking Control in OSCAR Single local rule for each leg[i]: Swing phase has constant length. Duration of stance phase determines velocity. Slightly different leg orientation depending on leg position (F, M, L). So far only one single rule implemented with circular neighborhood relation for all legs.

  11. Self-Organizing Gait Patterns in OSCAR Slow Gait Tetrapod Gait Tripod Gait Emergent Gait Patterns with Increasing Speed!

  12. Insect Walking with Lost Legs [Source: Holk Cruse, Uni Bielefeld]

  13. Leg Coordination After Leg Loss for OSCAR Same single rule, only neighborhood relation changes.

  14. OSCAR Walking with Lost Middle Legs Simulated Amputation

  15. ORCA Organic Robotic Control Architecture

  16. Curve Walking Towards a Goal

  17. All 6 legs intact Left leg lost Right leg lost Curve Walking with Lost Leg

  18. Conclusion • ORCA – Organic Robot Control Architecture • - BCUs (Basic Control Units) for functional control • OCUs (Organic Control Units) for monitoring, fault adaption and optimization • Learning based on hybrid crisp fuzzy methods and adaptive filters • OSCAR - Organic Self-Configuring and Adapting Robot • Self-organizing gait patterns inspired by insects • Monitoring of leg health status by sensor signals from joints and feet • Self-organizing gait patterns also in case of lost legs

  19. Current Work • Tools for hybrid crisp fuzzy methods (U Osnabrück) • Integration of learning methods into ORCA/OSCAR • New hardware platform for OSCAR (2 * ARM9 + 6 * ATmega32) • Additional sensors (inertial, ultrasonic, laser, inclinometer, camera, .. ) • Tolerance of less severe leg faults than complete leg losses • More sophisticated methods of anamoly detection (e. g. information theory, fuzzy) • Combination with obstacle avoidance in more challenging environments • Application of ORCA to other robot platforms (e .g. fish-like underwater robots) Acknowledgement of contributions of ORCA Group Members Werner Brockmann (U Osnabrück), Adam El-Sayed-Auf, Karl-Erwin-Grosspietsch (Fraunhofer AIS), Bojan Jakimovski, Marek Litza, Florian Mösch

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