1. Bastien DURAND Karen GODARY-DEJEAN – Lionel LAPIERRE Robin PASSAMA – Didier CRESTANI 27 Janvier 2011 ConecsSdf Architecture de contrôle adaptative : une plateforme robotique fiable 1

2. SYSTOL 2010 – October 6-8 2 Unmanned Grounded Vehicles Always and Often Fail.

3. SYSTOL 2010 – October 6-8 3 Low Reliability Need of Dependability 80% fatal failures 36% availability 80% fatal failures 36% availability How UGVs fail? Failure Physical Human Sensor Communications Power Control System DesignInteraction Effector 56% 26% 54% critical 9% [Carlson & Murphy 05]

4. SYSTOL 2010 – October 6-8 « Ability to deliver a service that can justifiably be trusted » 4 Fault Prevention Fault Removal Dependability Means Fault Tolerance Fault Forecasting Provide a correct service despite the presence of faults perturbing resources of the system Fault severity evaluation Fault Detection and Diagnosis System Recovery System Recovery Fault Avoidance Fault handling

5. SYSTOL 2010 – October 6-8 ROBOTS OFTEN AND ALWAYS FAIL  Dependability needs  Introduction of fault tolerance in robotics systems PROBLEMATIC HOW TO MANAGE FAULT TOLERANCE IN ROBOTICS ?

6. SYSTOL 2010 – October 6-8 Lack of generic, global and structured methodology to manage fault tolerance in robotic systems. 6 Dependability in robotics

7. SYSTOL 2010 – October 6-8 7 Dependability in robotics Methodology for reliability enhancement Autonomy sharing Experiment Conclusion Contents

8. SYSTOL 2010 – October 6-8 Methodology for reliability enhancement 8

9. SYSTOL 2010 – October 6-8 9 Used methods: FMCEA functional decomposition Ishikawa diagrams Identification of: Fault type Fault severity Step 1 : Identification Fault identification for the Drive function

10. SYSTOL 2010 – October 6-8 10 Use of existing methods Detection timing checks, reasonableness checks, safety-bag,.. Diagnosis Model based diagnosis Use of residuals Use of signature analysis Depend on the preceding step Step 2 : Detection & Diagnosis

11. SYSTOL 2010 – October 6-8 11 Adaptive recovery Functions of the context Functions of the severity Several types of recovery Step 3: recovery

12. SYSTOL 2010 – October 6-8 12 Autonomy: the ultimate goal of autonomous robotics No universal definition Evaluation autonomy levels, depending on the Human implication Focus on the recovery problem  Which one holds the adaptation decision ?  How to adapt ? Autonomy level adaptation could be an efficient answer for fault tolerance.

13. SYSTOL 2010 – October 6-8 13 When the robot detects a fault, If it can not resolve itself the problem, it could ask the human help: full autonomy ⇒ human/robot interaction. The human choose a new autonomy level  Easy autonomy level adaptation Autonomy level adaptation in our methodology

14. SYSTOL 2010 – October 6-8 14 Proposed recovery solutions Autonomy adjustment High severity Human Robot Interaction New autonomy level Local adaptation Weak severity: Algorithm reconfiguration Medium severity: Functioning mode adaptation Reach safe state & Stop Fatal severity

15. SYSTOL 2010 – October 6-8 Integration of the Methodology in a Control Architecture

16. SYSTOL 2010 – October 6-8 16 Module Sensors Module Actuators Module Sensors Module SCHEDULER LOCAL SUPERVISOR GLOBAL SUPERVISOR Mission Objective EXECUTIVE LEVEL DECISIONAL LEVEL Events 2 Levels Executive Decisional Modularity Decomposition of control scheme Scheduler Real time control of modules execution Initial Control Architecture COTAMA Sub-objective

17. SYSTOL 2010 – October 6-8 Module Sensors Module Actuators Module Sensors Module SCHEDULER LOCAL SUPERVISOR GLOBAL SUPERVISOR Mission Objective Sub-objective Events EXECUTIVE LEVEL DECISIONAL LEVEL 17 Observer Module Adapter Events ADAPTER SUPERVISOR Adapted Sub-objective Local Events Global Events Global Observation Module Data Base Update Event (Modules status) CONTEXTUAL SUPERVISOR Autonomy adjustment Detection DiagnosisRecovery Local adaptation Control Architecture Safe state & Stop

18. SYSTOL 2010 – October 6-8 Experimentation 18

19. SYSTOL 2010 – October 6-8 19 Human-Machine Interface Embedded Computer COTAMA Supervisory Control Mission Manager Human-Robot Interaction Manager WiFi LAN Experimental Context Mission Manager Users Operators Pioneer 3-DX Linux - RTAI MISSION Deliver objects between laboratory offices MISSION Deliver objects between laboratory offices

20. SYSTOL 2010 – October 6-8 20 Navigation Obstacle avoidance SMZ Guidance Communication with robot Monte Carlo localization Control Hardware Models bank Localization Path following Sonars Simulator Odometric localization Control Scheme

21. SYSTOL 2010 – October 6-8 Delivery mission = Drive + Take + Drive + Give 21 Autonomy levels: Autonomous Teleprogrammed Teleoperated Delivery mission Drive = Path generation + Path following Functioning modes: Optimal behavior Degraded behaviors Human/Robot Interaction mode

22. SYSTOL 2010 – October 6-8 22 Local Supervisor Autonomy Adjustment Human Robot Interaction Initiated by robot Decision made by operator New Map

23. SYSTOL 2010 – October 6-8 23 Adapter Supervisor Local Adaptation 2 Adaptation types Low failure  Set current algorithms parameters Medium failure  Switch to a degraded functionality

24. SYSTOL 2010 – October 6-8 24 A B 1 2 3 HIL Experimentation Error: localization and path following Severity: hard Recovery: autonomy adjustment Error: real time violation Severity: low Recovery: reconfiguration Delivery mission from A to B Still in A Laboratory map Path to follow

25. SYSTOL 2010 – October 6-8 25 A B 1 2 3 4 5 Teleoperation: HIL Experimentation Teleprogrammation: new path the operator see the obstacle

26. SYSTOL 2010 – October 6-8 26 A B 1 2 3 4 5 6 7 Error: Sonar failure Severity: medium Recovery: Degraded sub-objective Error: Collision event Severity: hard Recovery: Autonomy adjustment HIL Experimentation DELIVERY Despite many faults, the mission is successful. Teleoperation

27. SYSTOL 2010 – October 6-8 27 Autonomy adaptation No human intervention Ponctual human intervention Permanent human intervention

28. SYSTOL 2010 – October 6-8 28 We have proposed and implement a generic, global and structured methodology to identify, detect, diagnose and recover from faults. We proposed efficient recovery mechanisms to answer accurately to each situation. We applied this methodology on a case study. The autonomy sharing can be used to manage complex situations and to enhance fault tolerance for autonomous mobile robot. Conclusion

29. SYSTOL 2010 – October 6-8 29 Deeper reliability evaluation of the fault tolerance mechanisms : for the delivery mission through a long term experiment. for different types of missions. Study in details the autonomy sharing problematic (allocation of the final decision). Design a reliable robotic platform for “benchmark” experiments. Prospects

30. SYSTOL 2010 – October 6-8 The end Do you have any questions ? 30

31. SYSTOL 2010 – October 6-8 31 Experiment Conclusion Real timeSonarLocalizationCollision LowReconfiguration Medium Degraded sub-objective Hard Teleoperation and teleprogramation The mission is successful Several faults of different types have been managed Different recovery with Human Robot Interaction Severity Fault Recovery mechanism depending on fault and severity level

32. SYSTOL 2010 – October 6-8 32 Navigation Obstacle avoidance SMZ Guidance Communication with robot Monte Carlo localization Control Hardware Models bank Localization Path following Sonars Simulator Odometric localization Control Scheme 32

33. SYSTOL 2010 – October 6-8 Monitored system Detection & Diagnosis ActuatorsRobotSensors ObservationDiagnosis Fault