5. Underwater Manipulation

Transcription

1 Autonomous Robotic Manipulation (4/4) Pedro J Sanz sanzp@uji.es 5. Underwater Manipulation April 2010 Fundamentals of Robotics (UdG) 2 1

2 I-AUV DESIGN MECHATRONICS (Object Recovery) 2009 ENE FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DEC 2010 ENE FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DEC 2011 ENE FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DEC 5/9/2011 RAUVI DPI C03 TRIDENT (FP7-ICT ) Multipurpose Autonomous Manipulation Systems for Underwater Intervention Missions SOFTWARE EXPERIMENTS Timeline Project Timeline Preliminary Design 2 Final Design by Remotely Operated Vehicles (ROVs). Manned submersibles have the advantage of placing the operator in the field of operation with direct view to the object being manipulated. Their drawbacks are the reduced time for operation (typically in the order of a few hours) the human presence in a dangerous and hostile environment, and a very high cost of the associated oceanographic vessel. Work class ROVsare currently the preferred technology for deep water intervention. They can be remotely operated for days without problems. Nevertheless, they still need an expensive oceanographic vessel with a heavy crane and automat ic Tether Management System (TMS) and a Dynamic Position system (DP). The cognitive fatigueof theoperat or who has to takecareof theumbilical and the ROV while cooperating with the operator of the robotic arms is remarkable. For thesereasons, someresearchershaverecently started to think about the natural evolution of the intervention ROV, the Intervention AUV (I-AUV). Without the need for the TMS and the DP, light I-AUVs could theoretically be operated from cheap vessels of opportunity, considerably reducing the cost of operation. Considering 3D Simulation Teleoperated Fig. G Watertight casescontaining thecomputer (left) and a stereo-camera (right) of the vision system Mechatronics Autonomous Mechatronics Integration Sea Trials the manipulator architectures. Both of them have combined into a new schema that allows for reactive ROS messages. autonomous manner. and emits an acoustic signal that is audible wherethesurvey trajectory intersects, and to detect andbeen distance of approximately one kilometer. The the fast development of battery technology, and removing that periodically the operator from the control loop, one can start to and deliberative behaviors on both subsystems. Re- up to a acoustic beacon will begin to emit when immersed in water and the ping will last until the battery is exhausted, localize the ToI. Images are processed only once and then active actions are performed in the low-level control layer think about intervention operations that last for several days, where a ship is only needed on the first and the that communicates with the real or simulated I-AUV via around one month stored. later. The Alltime further limitation operations forces theare performed on the extracted an abstraction interface. On the other hand, the whole last day for launch and recovery. But this fascinating scenario, where I-AUVs do the in thisfeat paper, ures. wethefeat assume the uredescriptorsof FDR trol System (MCS), implemented using the Petri net for- search method to be as efficient as possible. For the experiments presented mission is supervised at a high-level by a Mission Con- a singleimagemalism. Visual perception services are provided by the work autonomously, comes at the cost of endowing the robot with the intelligence needed to keep the operator to have already been localized within a small area, and vision module described in Section 6. To integrate the we focus on the local typically vision-based occupy search in and therecovery. order of 100kB of memory, andheterogeneous computing hardware and software of all out of the control loop. Although standard AUVs are papers discuss black box recovery with system components, to allow for easy integration of ad- mission specific components, and to record all underwater the visual intervention system vehicle. adopts Allaexam- variety of heuristics Final to Architecture loadditional also operated without human intervention, they are constrained Few technical to survey operations, commonly flying at a safe the aid of an theliteraturedescribetheuseof ROV vehicles. To sensor input in a suitable playback format for simula- purposes, we use the ROS Robot Operating System authors only those knowledge, features an autonomous into main vehicle memory that have a hightion altitude with respect to the ocean floor while logging plesin data. I-AUVs must be operated in the close proximity the best of the 12/04/2011 HMI has never been used for a black box recovery mission, [6][7]. Vehicle control, the manipulator, and the vision of the seabed or artificial structures. They have to be able to identify the objects be manipulat ed and the likely due to the high probability complexityto ofmatch this task. against Only some thenext image. system are implemented as independent ROS nodes that intervention 3D tasks Simulator be undertaken, while safely moving are executed on their own independent hardware units DPI, Madrid theoretic papers are available that describe prospective within a cluttered work area. While I-AUVs are the nat - work [4]. For each feature, a descriptor is calculated from theand that communicate through ROS messages over an 4 onboard ethernet network. The general architecture is ural Fig. way9of Vision technological module progress, architecture they represent as a ROS an au-nodethentic research challenge for the Robotics community. two-dimensional Haar wavelet response in a number of Section 2 presents the evolution of the I-AUV concept The remainder of this paper is organized as follows. illustrated in Figure 6. For further detail see [8]. Moreover, the I-AUVs that have been developed until under development and introduces details of both the rectangular regions that surround the feature. A match now, and which have proven eld capabilities, are heavy Fig. 2 The GIRONA 500 AUV in a survey configuration. sion is the recovery of a Flight Data Recorder (FDR, also known as black-box) from a crashed airplane. Flight recorders aretypically equipped with a KHz pinger vehicle and the robot arm. Section 3 shows an overview 7 4 F ig. 5 T he integrat ed I-AUV prototype in a water tank. 3 T he Control A rchitecture The I-AUV control architecture is composed of two initially independent architectures: the underwater vehicle 4 The U ser I nt er face Fig. 6 An overview of the RAUVI software architecture. Communications through the network are implemented via described later in this section. Then, during the second stage, the I-AUV navigates again to the RoI, localizes the target and executes the intervention mission in an TheGraphical User Interface (GUI) isused tospecify both the survey path and the intervention task. The former is done by loading a geo-referenced map of the area and indicating a set of waypoints (possibly using predefined grid-shaped trajectories). Thewaypointsaresent to the vehicle control system that guides the robot through them. Figure 7a shows an example of a grid-shaped trajectory superposed on a generated mosaic obtained during the experiments described later in this paper. Once the mosaic has been built, the user first looks for the target of interest on it. After selecting the target, the in- 2

3 I-AUV Architecture Reconfigurable Autonomous Underwater Vehicles for Intervention R a u v i / 12/04/2011 DPI, Madrid 5 I-AUV Integration (UdG, March 2011) Reconfigurable Autonomous Underwater Vehicles for Intervention R a u v i / 12 ABR 2011 Jornadas DPI 6 3

4 MAIN RESULTS ABOUT MANIPULATION 1. A complete arm-hand system is now ready to use for working standalone or integrated over the developed AUV GIRONA 500. IEEE Trans. on Mechatronics (2011) 2. Autonomous Manipulation Methodology Adapted to Underwater Scenarios Autonomous Robots (2; 2010) Robotic and Autonomous Systems (2011 ) 3. HMI & 3D Simulator Intelligent Service Robotics (2011) April 12, 2011 DPI, Madrid 7 MECHATRONICS 2009 NOV 2009 JAN 2010 JUL 2010 Comparative Study Hydraulic Arm (Robotnik) ARM 5 E (CSIP) Meeting (CSIP, UK) Light-Weight ARM 5 E (CSIP) April 12, 2011 DPI, Madrid 8 4

5 MECHATRONICS D-H PARAMETERS April 12, 2011 DPI, Madrid 9 Manipulation SOFTWARE April 12, 2011 DPI, Madrid 10 5

6 (1) SURVEY 5/9/2011 HMI & 3D Simulator (1) (2) (2) INTERVENTION April 12, 2011 DPI, Madrid 11 Marine Robot and Dexterous Manipulatin for Enabling Multipurpose Intevention Missions WP7 Multisensory Based Manipulation Architecture GIRONA st Year Review Meeting Pedro J Sanz IRS Lab 6

7 ESCENARIOS 2011 ENE FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DEC 2012 ENE FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DEC 2013 ENE FEB 5/9/2011 Intervention Scenarios Object recovery from a fixed base manipulator Annex 1 (p 57) Annex 1 (p 57) Object recovery from the free floating I-AUV developed system M2 M3 M5 Object recovery from the available I-AUV (stationkeeping)?? FIXED-BASE MANIPULATION I-AUV READY FREE-FLOATING MANIPULATION Isolated objects Target perturbations Perturbations on the base Water tank conditions Overlapped objects Control panel operations Avoiding obstacles Water tank conditions Level of complexity + Overlapped objects Control panel operations Avoiding obstacles Seabed conditions WP s Relationships WP9: Project Coordination and Management UJI WP8: Dissemination, Education and Training UdG WP5: Floating Manipulation UNIGE-ISME WP1: Navigation and Mapping UdG WP7: Multisensory Based Manipulation Architecture UJI WP3: Vehicles Intelligent Control Architecture HWU WP2: Single and Multiple Vehicles Control IST WP6: Hand+Arm Mechatronics System and Control UNIBO WP4: Visual/Acoustic Image Processing UIB May 5th, 2011 UdG, SPAIN 14 7

8 WP 7 Long Term Objective Related with Increasing the performance, focused on the physical interaction problem Grasping / manipulation in realistic conditions conditions (i.e. overlapping, bad visibility, etc.) First experiments, in controlled conditions (e.g. recovery a specific object has been demonstrated WP 7 The Aim A new methodology for multipurpose manipulation was successfully proved and now we want to adapt it within underwater robotics scenario May 5th, stAPR, Girona 16 8

9 T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) The Physical Interaction Framework Methodology [Prats et al., 2010] Multisensory-based framework for the specification and robust control of physical interaction tasks, where the grasp and the task are jointly considered on the basis of the Task Frame Formalism (TFF) [Bruyninckx & De Schutter, 96] and the Knowledge-based approach to grasping [Stansfield, 91] T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) Our previous work 9

10 T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) Moving to underwater The physical interaction frames May 5th, T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) Under-constrained grasps Do not fix all the 6 DOF for the grasp Use them for secondary tasks Keep the hand in the camera view Avoid occluding the object Control center of gravity May 5th,

11 T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) The grasp is based on the knowledgebased approach to grasping [Stansfield, 91] Let P = m 0, m 1,, m n represent a hand preshape (either prehensile or non-prehensile), where m i is the desired value for each of the n DOF's of the hand. The grasp is then defined as: H G = P, H, G, M G, S C S C is a a 6 x 6 diagonal selection matrix May 5th, T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) The physical interaction frames for a grasping action S c = diag 1, 1, 1, 1, 0, 1 May 5th,

12 T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) The consideration of under-constrained grasps presents several advantages in the context of underwater manipulation Real-time low-level grasp synthesis controllers will be able to exploit this free DOF in order to achieve a suitable configuration of the whole vehicle-arm kinematic chain. May 5th, T7.1 Sensor Integration [UJI 8]. Months 1 to 12 (12 months) Task Specification The task requires performing compliant motion, following a set of velocity-force references defined in the task frame, according to the TFF. It is defined as follows: T = T, v T, f T, S f May 5th,

13 T7.2 Specification of interfaces [UJI 11] [UNIBO 1] [UNIGE-ISME 1]. Months 7 to 18 (11 months) ROS interfaces (rxgraph output) May 5th, stAPR, Girona 25 Del. no. Deliverable name Dissemination level D7.1 Technical report on the methodology aspects and requirements on the Multisensory and knowledge-based approach architecture for grasping and dexterous manipulation Milestone no. Milestone name Delivery date Comments Means of verification 2 Object recovery from a fixed base manipulator PU 18 Delivery date (proj.month) 18 Experimental results using a fixed industrial robot arm, but simulating the underwater conditions May 5th, stAPR, Girona 26 13

14 Realism The Roadmap Manipulator inside the water under disturbances Fixed manipulator 3D Simulator Complexity May 5th, stAPR, Girona 27 Initial concept May 5th, stAPR, Girona 28 14

15 Initial concept Current concept May 5th, stAPR, Girona 29 Visual Tracking and control for manipulation Simulation experiments on grasping under vehicle disturbances The hand is controlled in order to keep a given relative pose with respect to the object 15

16 Underwater simulation 5/9/2011 Underwater simulation Simulated I-AUV Virtual camera sensor Virtual joint position sensors Two arm models: 5DOF and 7 DOF ROS interfaces: Set/Get vehicle pose Set/Get arm joints Get camera image May 5th, stAPR, Girona 32 16

17 1 st Successful Experiments on Tracking, Visual Servoing and arm control for grasping Vision sensors Force/torque sensor Tactile sensors M2 Object recovery from a fixed-base manipulator May 5th, stAPR, Girona 33 Object recovery from a fixed base manipulator (M2) CALIBRATION APPROACHING GRASPING 17

18 Autonomous hooking sequence of a flight data recorder prototype in water tank conditions, with the arm mounted on an aluminium structure and under manual disturbances WP 7 Dissemination EURON 10 th G. Giralt PhD Award : PhD Thesis of M. Prats 1. M. Prats, P.J. Sanz and A.P del Pobil. A framework for compliant physical interaction: the grasp meets the task. Journal of Autonomous Robots (Special Issue on autonomous mobile manipulation), 28(1), pp , M. Prats, P.J. Sanz and A.P. del Pobil. Reliable non-prehensile door opening through the combination of vision, tactile and force feedback. Journal of Autonomous Robots, 29(2), pp , August M. Prats, J.C. García, R. Marin and P.J. Sanz. Autonomous Grasping in Underwater Environments: A Case Study on the Object Recovery Problem. Special Issue "Autonomous Grasping"- Robotic and Autonomous Systems Journal. (Submitted, January 2011). 4. M. Prats, D. Ribas, N. Palomeras, J. C. García, V. Nannen, J. J. Fernández, J. P. Beltrán, R. Campos, P. Ridao, P. J. Sanz, G. Oliver, M. Carreras, N. Gracias, R. Marín, A. Ortiz. Reconfigurable AUV for Intervention Missions: A Case Study on Underwater Object Recovery. Journal of Intelligent Service Robotics, Sp. Issue on Marine Robotic Systems. (Submitted, March 2011). 5. J. J. Fernández, M. Prats, P. J. Sanz, J. C. García, R. Marín, and Mike Robinson. A New Underwater Robot Arm for Shallow Water Intervention Missions. IEEE/ASME Trans. on Mechatronics, Focused Section on Marine Mechatronic Systems. (Submitted, April 2011) 18