UNDERWATER COOPERATIVE MANIPULATION AND TRANSPORTATION Giuseppe Casalino

1. UNDERWATER COOPERATIVE MANIPULATION AND TRANSPORTATION Giuseppe Casalino http://www.isme.unige.it After the development of some pioneering projects during the nineties, the topic of underwater manipulation, and in particular cooperative manipulation and transportation, to be performed under floating conditions and within different types of cooperative forms, is now receiving an increasing attention by part of the research community, in the perspective of transferring the relevant technologies toward different underwater intervention applications, of both civil and commercial types. In this perspective the talk will provide an overview of the control and coordination problem which as been afforded by ISME (within different collaborative projects of both international and national type). Now available control and coordination results, near to be transferred toward practical applications, will be outlined; then followed by a presentation the on-going research activities, addressing the extension of cooperative control methodologies to more complex underwater intervention scenarios foreseeable for the near future.

2. ISME Brief - Established in 1999 - Universitymembers Ancona Cassino Genova Lecce Pisa Firenze Genova Genova Firenze Firenze Ancona Ancona Pisa Pisa Cassino Cassino Lecce Lecce - > 30 researchers Sharedinfrastructures lab, equipements

3. ISME Brief • Robotics - Underwater manipulation systems - Guidance and control of AUV’s and ROV’s - Distributed coordination and control of AUV’s team - Mission planning and control • Underwater acoustics - Acoustic localization - Acoustic communications - Underwater optical communications, - Acoustic Imaging and Tomography - Seafloor acoustics - Sonar systems • Signal Processing and data acquisition - Distributed data acquisition - Geographical information systems - Decision support systems - Classification and data fusion Applications: - Surface and underwater security systems - Distributed underwater environmental monitoring - Underwater archaeology - Underwater infrastructures inspection - Sea surface remote sensing

4. Autonomy in UW-InterventionRobotics PastHistory Universityof Hawaii at Manoa ODIN (1994 - )

5. Autonomy in UW InterventionRobotics PastHistory Stanford University Aerospace and RoboticLab. OTTER (1995 - )

6. Autonomy in UW InterventionRobotics PastHistory Ifremere, Toulon UNION (1995- )

7. Autonomy in UW InterventionRobotics PastHistory AMADEUS (1997-1999) Universityof Genova – DIST Graal-lab IAN CNR, Genova Robotic-Lab Heriot Watt University Edinburg Ocean System-lab

8. Autonomy in UW InterventionRobotics RecentHistory Universityof Hawaii at Manoa SAUVIM (1997-2009) 1-Undock from the piertoreach the center of the harbour 2-Search for the submerged item 3- Navigate and dive toward the item 4- Hover in the proximityof the detected item 5- Start the autonomousmanipulation (hook a recoverytoolto the target, cut a rope) 6- Otimize the workspaceduringmanipulation 7- Dock the arm and back for re-docking the pier

9. Autonomy in UW InterventionRobotics RecentHistory ALIVE (2001-2003) Cyberbernetix Company Marseille Ifremere, Toulon HW University, Edinburg Ocean System lab

10. Autonomy in UW InterventionRobotics Nowdays RAUVI (2009-2012) Universitat JaumePrimero Universitat De IllesBalears UniversityofGirona • DirectlyInspired from SAUVIM • MuchLightermechanicalassembly • Consequently prone for “Agility” • (concurrentcoordinatedVehicle-armmotions) • Sequentialmotionswerehoweverused

11. Autonomy in UW InterventionRobotics Nowdays Universitat JaumePrimero Heriot Watt University TRIDENT (2010-2013) Universitat De IllesBalears Universityof Genova UniversityofGirona Universityof Bologna Istituto Superiore tecnico Graaltech s.r.l. Genova • DirectlyInspired from SAUVIM • MuchLightermechanicalassembly • Consequently prone for “Agility” • (concurrentcoordinatedVehicle-armmotions) • “Agilty” achievedv ia Multi-task Priority • Dynamic Programming Basedapproach • Unifiedscalabledistributed control architecure • Allowstasks to be added-subtracted, even “on-fly”, • witinvariantalgorithmicstructure

12. TRIDENT Project Simulations and field trials 3 4 2 5 Simulation Including vehicle & arm dynamic control layer Autonomous in the pool Teleoperated in the pool Autonomous in the sea

13. TRIDENT Project Functional Control Architecture High Level Mission Commands Kinematic Control Layer KLC Arm Vehicle Dynamic Control Layer DLC Arm Vehicle Vehicle Sensors & Actuation System Interface Global Physical System

14. TRIDENT Project Objective-priority-based Control Technique Micro Priorities Macro priorities 1 Inequality objectives 1 Camera centering 1 Camera distance 1 Camera height 3 Joint limits 4 Manipulability 5 Horizontal attitude 2Equality objectives 1 End-effector approach (distance) 2 End-effector approach (orientation) 3 Sub-system motions 1 Arm 2 Vehicle

15. Single-ArmFloatingManipulators Summary of achievedresults 1- A unifiedalgorithmic control frameworkhasbeenassessed 2- Simulationexperimentshavebeensuccessfull 3- Control architecture and relatedRTalgorithmicSwhasbeenimplemented 4- Field trials at pool successfull 5- Field trials atseasuccessfull 6- Refinementsrelated with discontinuity-avoidance in referencesyestemvelocities havebeenrecentlyproduced

16. Dual Arm Extension-1 • AlgorithmiccontrolFramework: • Direct extension from the Single arm case • Embedding Single Arm case a as special one • Additionalaspects: • The vehiclevelocitymustnowbeassigned in ordertosuitablycontribute the motionsofbotharms

17. Dual-arm Extension-2 • AlgorithmiccontrolFramework: • Direct extension from Extension-1 • EmbeddingSingel- arm and extension-1 as special cases • Additionalaspects: • The graspingconstraints must be guaranteedfulfileldalltimes • Object stressesshpould be avoided or minimized • The vehiclevelocitymustagainbeassigned in ordertosuitablycontribute the motionsofbotharms, in turn consrainedby the graspedobject

18. Dual-arm Extension-2 Preliminary simulation of a purely kinematic model 6

19. Dual-arm extension-3 Dual-arm floating assembly • AlgorithmiccontrolFramework: • Direct extension from dual-armpreviousones • Butlargelyindependentfrom base motion • (assemblyduringtransportastion? Whynot?) • Additionalaspects: • More extensiveuseof vision (forrelativellocalizationof the matingparts) • More extensiveuseofforce-feedback (fordriving the mating once the contactshavebeenestablished

20. Dual-arm extension-3 Non-floating dual arm Peg-in-hole Early AMADEUS Project Experiments (1997-1999)

21. Cooperative Extension • AlgorithmiccontrolFramework: • Stll a directextension of the previous • Embeddingthe previousas special cases • Additionalaspects: • Graspingconstraints must be guaranteedfulfilleldalltimes • Object stressesto be avoided or minimized • MutualLocalizationisneeded (at leastforavoidingvehiclescollision) • Some controlparametershavetobesharedCommunication • Control performances to be tuned with the MCIS communicationbandwidt • (lowerbandwidth-slowerresponces) • An optimizedMCIS Management System (MCIS-MS), maximallyguarantetingcoordinated cooperative control performances, needs to be developed MCIS Min. Common Info Set

22. Cooperative Extension Minimize explicit comm. Maximize Implicit comm. expl.on Fully Centralized approaches NOT feasible Very scarce communication allowed Geometric constraint Minimize object stressed Dually use object stresses

23. Cooperative extension Preliminary simulation and experiments of purely kinematic grounded models (Total communication allowed) 7 8

24. Autonomy in UW InterventionRobotics Nowdays GENOVA Cooperative Control MARIS (20013-2016) PISA Communications CASSINO Dynamic Control + LECCE Navigation GENOVA Integration Mission planning BOLOGNA GrippersF/T sensing PARMA Vision

25. A Foreseable Road-MAP 3 0 1 2 4 5 MCIS-MS MCIS Management System

26. END Giuseppe Casalino: full prof. on Robotics Dist-Universityof Genova, Italy Via Opera Pia 13 Genova 16145, Italy casalinp@dist.unige.it