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Underwater manipulation

This document discusses underwater manipulation and robotic systems. It provides an overview of ISME, an Italian research unit, and some of its projects related to underwater robotics including MARIS, which aims to develop autonomous marine robotics for intervention tasks. It also discusses the state of the art in underwater manipulation, challenges including kinematics and dynamics when coordinating a vehicle and manipulator underwater. Various tasks that could be controlled are mentioned such as end effector position and orientation, joint limits, drag minimization, and obstacle avoidance.

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Underwater manipulation Gianluca Antonelli Universit`a di Cassino & ISME antonelli@unicas.it http://webuser.unicas.it/lai/robotica http://www.isme.unige.it http://www.eng.docente.unicas.it/gianluca antonelli Gianluca Antonelli Biograd Na Moru, 8 October 2015
ISME in brief Italian Joint Research Unit established in 1999 Sites: Ancona Cassino Firenze Genova Lecce Pisa Gianluca Antonelli Biograd Na Moru, 8 October 2015
ISME in brief SEA Lab Joint Italian Navy/ISME located in La Spezia No need of advance area clearance Availability of Navy support personnel Some restrictions (activities/personnel to be listed in advance, no working at nights. . . ) Gianluca Antonelli Biograd Na Moru, 8 October 2015
ISME in brief A selected map of projects logos. . . Gianluca Antonelli Biograd Na Moru, 8 October 2015
Marine Autonomous Robotics for InterventionS PRIN2010-2011 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Marine Autonomous Robotics for InterventionS PRIN2010-2011 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Effective Dexterous ROV Operations in Presence of Communications Latencies H2020-BG-2014 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Effective Dexterous ROV Operations in Presence of Communications Latencies H2020-BG-2014 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Robotic subsea exploration technologies H2020-SC5-2014 mineral and raw material exploration and recovery (in negotiation) Gianluca Antonelli Biograd Na Moru, 8 October 2015
Outline Motivation Inverse Kinematics A possible kinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
Applications Where uw manipulation is used/needed: Oil & gas industry Renewable energy Power/communication cables Fisheries & aquaculture Archaeology Security Natural science/biology Decommissioning Diver assistance Gianluca Antonelli Biograd Na Moru, 8 October 2015
State of the art aged approach off-shore operator acts on the vehicle off-shore operator acts on the arm motors (!) voice coordination between the two manned visual feedback Gianluca Antonelli Biograd Na Moru, 8 October 2015
State of the art Recent approach vehicle in automatic station keeping or docked off-shore operator with a master/slave architecture Gianluca Antonelli Biograd Na Moru, 8 October 2015
State of the art Effort for working class vehicles 13 peoples on 24 hours 4 to 6 weeks ROV crew work 12 hours a day - 7/7 1 day of operation costs 100 ÷ 300 ke Gianluca Antonelli Biograd Na Moru, 8 October 2015
Objective Autonomously (as much ass possible. . . ) achieve complex operations Gianluca Antonelli Biograd Na Moru, 8 October 2015
Space, aerial and underwater vehicle-manipulators DLR Canadian Space Agency ALIVE normal robots but floating base kinematic coupling dynamic coupling unstructured environment Gianluca Antonelli Biograd Na Moru, 8 October 2015
Floating robots kinematics Oi η1 ηee ❅❅❘ end-effector velocities ❍❍ ❍❍ ❍❍ ❍❍ ❍❍ ❍❍❥ Jacobian system velocities˙ηee = ˙ηee1 ˙ηee2 = J(RI B, q)ζ ζ =   ν1 ν2 ˙q   Gianluca Antonelli Biograd Na Moru, 8 October 2015
UVMS dynamics in matrix form M(q)˙ζ + C(q, ζ)ζ + D(q, ζ)ζ + g(q, RI B) = τ formally equal to a ground-fixed industrial manipulator 1 however. . . Model knowledge Bandwidth of the sensor’s readings Vehicle hovering control Dynamic coupling between vehicle and manipulator External disturbances (current) Kinematic redundancy of the system 1 [Siciliano et al.(2009)Siciliano, Sciavicco, Villani, and Oriolo] [Fossen(2002)] [Schjølberg and Fossen(1994)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Dynamics Movement of vehicle and manipulator coupled movement of the vehicle carrying the manipulator law of conservation of momentum Need to coordinate at velocity level ⇒ kinematic control at torque level ⇒ dynamic control 2 2 [McLain et al.(1996b)McLain, Rock, and Lee] [McLain et al.(1996a)McLain, Rock, and Lee] Gianluca Antonelli Biograd Na Moru, 8 October 2015
A first solution Assuming the vehicle in hovering is not the best strategy to e.e. fine positioning3, better to kinematically compensate with the manipulator 3 [Hildebrandt et al.(2009)Hildebrandt, Christensen, Kerdels, Albiez, and Kirchner] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Outline Motivation Inverse Kinematics A possible kinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control scheme second. tasks ηd, qd τ η, q IK main task Control Output of IK (Inverse Kinematics) is the position/velocity to be controlled by the actuators (vehicle thrusters and joints’ torques) Btw, torque level usually not available ⇒ kinematic controller Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ Starting from a generic m-dimensional task (e.g., the e.e. position) σ = f(η, q) ∈ Rm ˙σ = J(η, q)ζ An inverse mapping is required Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ ✖✕ ✗✔ ■ Starting from a generic m-dimensional task (e.g., the e.e. position) σ = f(η, q) ∈ Rm ˙σ = J(η, q)ζ An inverse mapping is required Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills A robotic system is kinematically redundant when it possesses more degrees of freedom than those required to execute a given task Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills A robotic system is kinematically redundant when it possesses more degrees of freedom than those required to execute a given task Redundancy may be used to add additional tasks ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ ✖✕ ✗✔ ■ Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills A robotic system is kinematically redundant when it possesses more degrees of freedom than those required to execute a given task Redundancy may be used to add additional tasks ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ ✖✕ ✗✔ ■ ˙σa ✚✙ ✛✘ ˙σb ✙ ✖✕ ✗✔ ✶ Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills Classical example: control e.e. position while reconfiguring the structure with internal motion Kuka Iiwa Gianluca Antonelli Biograd Na Moru, 8 October 2015
Kinematic control in pills In the redundant case, the equation ˙σ = Jζ is solved by ζ = JT JJT −1 J† ˙σ + I − J† J N ζo i.e., by a pseudoinverse and an arbitrary vector projected onto the null-space need for closed-loop also. . . Gianluca Antonelli Biograd Na Moru, 8 October 2015
Handling several tasks Extended Jacobian4 Add additional (6 + n) − m constraints h(η, q) = 0 with associated Jh such that the problem is squared with ˙σ 0 = J Jh ζ 4 [Chiaverini et al.(2008)Chiaverini, Oriolo, and Walker] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Handling several tasks Augmented Jacobian An additional task is given σh = h(η, q) with associated Jh such that the problem is squared with ˙σ ˙σh = J Jh ζ Gianluca Antonelli Biograd Na Moru, 8 October 2015
Handling several tasks Task priority redundancy resolution σh = h(η, q) with associated Jh further projected on the the null space of the higher priority one ζ = J† ˙σ + Jh I − J† J † ˙σh − JhJ† ˙σ Also known as the exact solution with close similarities to the convex-optimization-based methods Gianluca Antonelli Biograd Na Moru, 8 October 2015
Handling several tasks Singularity robust task priority redundancy resolution 5 σh = h(η, q) with associated Jh further projected on the the null space of the higher priority one ζ = J† ˙σ + I − J† J J† h ˙σh 5 algorithmic singularities here. . . [Chiaverini(1997)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Handling several tasks Behavioral algorithms (behavior=task), bioinspired, artificial potentials, neuro-fuzzy, cognitive approaches, etc. btw. . . mood ? Gianluca Antonelli Biograd Na Moru, 8 October 2015
Geometrical meaning of the null-space ˙σ = J ˙ζ with m = 1 and n = 2 is a line! (left) Range of the pseudoinverse and the null spaces are orthogonal (right) Gianluca Antonelli Biograd Na Moru, 8 October 2015
Comparison between exact and robust solutions ζ = J† ˙σ + J h I − J† J † ˙σ h − J hJ† ˙σ ζ = J† ˙σ + I − J† J J† h ˙σ h Gianluca Antonelli Biograd Na Moru, 8 October 2015
Comparison between exact and robust solutions ζ = J† ˙σ + J h I − J† J † ˙σ h − J hJ† ˙σ ζ = J† ˙σ + I − J† J J† h ˙σ h Gianluca Antonelli Biograd Na Moru, 8 October 2015
Comparison between exact and robust solutions ζ = J† ˙σ + J h I − J† J † ˙σ h − J hJ† ˙σ ζ = J† ˙σ + I − J† J J† h ˙σ h Gianluca Antonelli Biograd Na Moru, 8 October 2015
Some issues Kinematic singularities Damped least square Singular-value-decomposition-based filtering Other kind of filtering Algorithmic singularities Two different-priority tasks are achievable alone but not together: ranks of both J and Jh is full but not of J Jh (still the inversion of a singular matrix) Set-based/inequality control 6 Task transition vs continuity/priority 6 [Escande et al.(2013)Escande, Mansard, and Wieber, Simetti et al.(2013)Simetti, Casalino, Torelli, Sperind´e, and Turetta, Antonelli et al.(2015)Antonelli, Moe, and Pettersen] Gianluca Antonelli Biograd Na Moru, 8 October 2015
But. . . What are these tasks we are talking about ? Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Given 6 + n DOFs and m-dimensional tasks: End-effector position, m = 3 pos./orientation, m = 6 distance from a target, m = 1 alignment with the line of sight, m = 2 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Manipulator joint-limits several approaches proposed, m = 1 to n, e.g. h(q) = n i=1 1 ci qi,max − qi,min (qi,max − qi)(qi − qi,min) Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Drag minimization, m = 1 7 h(q) = DT (q, ζ)W D(q, ζ) within a second order solution ˙ζ = J† ¨σ − ˙Jζ − k I − J† J ∂h ∂η ∂h ∂q + ∂h ∂ζ 7 [Sarkar and Podder(2001)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Manipulability/singularity, m = 1 h(q) = det JJT (In 8 priorities dynamically swapped between singularity and e.e.) joints inhibited direction singularity singularity setclose to 8 [Kim et al.(2002)Kim, Marani, Chung, and Yuh, Casalino and Turetta(2003)] [Chiacchio et al.(1991)Chiacchio, Chiaverini, Sciavicco, and Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Restoring moments: m = 3 keep close gravity-buoyancy of the overall system 9 m = 2 align gravity and buoyancy (SAUVIM is 4 tons) 10 fb fg τ 2 9 [Han and Chung(2008)] 10 [Marani et al.(2010)Marani, Choi, and Yuh] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Obstacle avoidance m = 1 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Workspace-related variables Vehicle distance from the bottom, m = 1 Vehicle distance from the target, m = 1 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Sensors configuration variables Vehicle roll and pitch, m = 2 Misalignment between the camera optical axis and the target line of sight, m = 2 Gianluca Antonelli Biograd Na Moru, 8 October 2015
Tasks to be controlled Visual servoing variables Features in the image plane 11 11 [Mebarki et al.(2013)Mebarki, Lippiello, and Siciliano, Mebarki and Lippiello(in press, 2014)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Outline Motivation Inverse Kinematics A possible kinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
Behavioral control in pills Inspired from animal behavior sensors behavior a actuators behavior b actuators behavior c actuators How to combine them in one single behavior? Gianluca Antonelli Biograd Na Moru, 8 October 2015
Behavioral control in pills Inspired from animal behavior sensors behavior a actuators behavior b actuators behavior c actuators How to combine them in one single behavior? Gianluca Antonelli Biograd Na Moru, 8 October 2015
Competitive behavioral control Behaviors are in competitions and the higher priority can subsume the lower ones12 sensors behavior b ζ2 behavior a ζ1 behavior c ζ3 ζd 12 [Brooks(1986)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Cooperative behavioral control Behaviors cooperate and the priority is embedded in the gains13 sensors behavior b ζ2 α2 behavior a ζ1 supervisor α1 behavior c ζ3 α3 ζd 13 [Arkin(1989)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Competitive-cooperative and tasks conflicting Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB Null Space-based Behavioral control Each action is decomposed in elementary behaviors/tasks motion reference command for each task ζd = J† ˙σd + Λσ σ = σd−σ Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Merging different tasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + J† b ˙σb,d + Λbσb primary secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Merging different tasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + I − J† aJa J† b ˙σb,d + Λbσb primary null space secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Merging different tasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + I − J† aJa J† b ˙σb,d + Λbσb primary null space secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Merging different tasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + I − J† aJa J† b ˙σb,d + Λbσb primary null space secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb ζd = ζa + Naζb Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Three-task example ζa = J† a ˙σa,d + Λaσ1 ζb = J† b ˙σb,d + Λbσ2 ζc = J† c ˙σc,d + Λcσ3 Successive projection approach Na = I − J† aJa Nb = I − J† bJb ζd = ζa + Naζb + NaNbζc Augmented projection approach Jab = Ja Jb Nab = In − J† abJab ζd = ζa + Naζb+Nabζc Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Three-task example ζa = J† a ˙σa,d + Λaσ1 ζb = J† b ˙σb,d + Λbσ2 ζc = J† c ˙σc,d + Λcσ3 Successive projection approach Na = I − J† aJa Nb = I − J† bJb ζd = ζa + Naζb + NaNbζc Augmented projection approach Jab = Ja Jb Nab = In − J† abJab ζd = ζa + Naζb+Nabζc Gianluca Antonelli Biograd Na Moru, 8 October 2015
NSB: Three-task example ζa = J† a ˙σa,d + Λaσ1 ζb = J† b ˙σb,d + Λbσ2 ζc = J† c ˙σc,d + Λcσ3 Successive projection approach Na = I − J† aJa Nb = I − J† bJb ζd = ζa + Naζb + NaNbζc Augmented projection approach Jab = Ja Jb Nab = In − J† abJab ζd = ζa + Naζb+Nabζc Gianluca Antonelli Biograd Na Moru, 8 October 2015
From behaviors to actions sensing/perception elementary behaviors actions commands supervisor Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: move to goal with obstacle avoidance obstacle avoidance σ1 = p − po ∈ R σ1,d = d J1 = ˆrT ∈ R1×2 ˆr = p − po p − po ζ1 = J† 1λ1 (d − p−po ) N(J1) = I − J† 1J1 = I − ˆrˆrT move to goal σ2 = p ∈ R2 σ2,d = pg J2 = I ∈ R2×2 ζ2 = Λ2 pg − p Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: competitive approach Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: competitive approach ❆ ❆ ❆ ❆ ❆❯ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: competitive approach ❄ only obstacle avoidance Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: competitive approach ❄ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: cooperative approach Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: cooperative approach ❆ ❆ ❆ ❆❯ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: cooperative approach ❇ ❇ ❇❇◆ linear combination: higher task is corrupted Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: cooperative approach ❄ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: NSB Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: NSB ❆ ❆ ❆ ❆❯ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: NSB ❇ ❇ ❇◆ null-space-projection: higher task is fulfilled Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simple comparison: NSB ❄ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
Gain tuning Cooperative task a b c situation 1 α1,1 α1,2 α1,3 sit. 2 α2,1 α2,2 α2,3 sit. 3 α3,1 α3,2 α3,3 sit. 4 α4,1 α4,2 α4,3 NSB Each behavior tuned as if it was alone but in each situation the priority needs to be designed Gianluca Antonelli Biograd Na Moru, 8 October 2015
Gain tuning Cooperative task a b c d situation 1 α1,1 α1,2 α1,3 α1,4 sit. 2 α2,1 α2,2 α2,3 α2,4 sit. 3 α3,1 α3,2 α3,3 α3,4 sit. 4 α4,1 α4,2 α4,3 α4,4 NSB Each behavior tuned as if it was alone but in each situation the priority needs to be designed Gianluca Antonelli Biograd Na Moru, 8 October 2015
Gain tuning Cooperative task a b c situation 1 α1,1 α1,2 α1,3 sit. 2 α2,1 α2,2 α2,3 sit. 3 α3,1 α3,2 α3,3 sit. 4 α4,1 α4,2 α4,3 NSB Each behavior tuned as if it was alone but in each situation the priority needs to be designed Gianluca Antonelli Biograd Na Moru, 8 October 2015
Stability analysis Lyapunov function14 V (˜σ) = 1 2 ˜σT ˜σ > 0 where ˜σ = ˜σT a ˜σT b ˜σT c T ˙V = −˜σT   Ja Jb Jc   v = −˜σT M ˜σ = −˜σT       Λa Oma,mb Oma,mc JbJ† aΛa JbNaJ† bΛb JbNJ† cΛc JcJ† aΛa JcNaJ† bΛb JcNJ† cΛc       ˜σ ˙V < 0 depending on the mutual relationships among the Jacobians 14 [Antonelli(2009)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Interaction Gianluca Antonelli Biograd Na Moru, 8 October 2015
Outline Motivation Inverse Kinematics A possible kinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
Numerical simulation on MARIS model: underwater 6-DOF vehicle + 7-DOF manipulator Reach a pre-grasp configuration in terms of end-effector position and orientation priority-1 task: e.e. configuration (m = 6) priority-2 task: vehicle roll+pitch (m = 2) priority-3 task: position of joint 2 (m = 1) only e.e. ⇒ complete solution ⇒ Gianluca Antonelli Biograd Na Moru, 8 October 2015
Numerical simulation on MARIS model: underwater 6-DOF vehicle + 7-DOF manipulator Cameraman action: keep the object in the field of view priority-1 task: field of view (m = 2) priority-2 task: vehicle roll+pitch (m = 2) priority-3 task: arm manipulability (m = 1) priority-4 task: mechanical joint limits (m = 7) animation ⇒ Gianluca Antonelli Biograd Na Moru, 8 October 2015
Simulations and experiments within TRIDENT [Simetti et al.(2013)Simetti, Casalino, Torelli, Sperind´e, and Turetta] Gianluca Antonelli Biograd Na Moru, 8 October 2015
Numerical simulation on MARIS model: interaction within the task-priority approach An impedance external loop is designed to push a button Σ0 ΣI Σee Gianluca Antonelli Biograd Na Moru, 8 October 2015
Numerical simulation on MARIS model: interaction within the task-priority approach An impedance external loop is designed to turn a valve Σ0 ΣI Σee have a look at the experiments made by Pedro Sanz, Pere Ridao and colleagues within TRIDENT Gianluca Antonelli Biograd Na Moru, 8 October 2015
The presented results are the outcome of the work of several colleagues from the University of Cassino, the Consortium ISME and PRISMA, the projects DEXROV and MARIS Filippo Arrichiello, Elisabetta Cataldi, Stefano Chiaverini, Paolo Di Lillo ISME PRISMA Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography I G. Antonelli. Stability analysis for prioritized closed-loop inverse kinematic algorithms for redundant robotic systems. IEEE Transactions on Robotics, 25(5):985–994, October 2009. G. Antonelli. Underwater robots. Springer Tracts in Advanced Robotics, Springer-Verlag, Heidelberg, D, 3rd edition, January 2014. G. Antonelli, S. Moe, and K. Pettersen. Incorporating set-based control within the singularity-robust multiple task-priority inverse kinematics. In 23th Mediterranean Conference on Control and Automation, pages 1132–1137, Torremolinos, S, June 2015. Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography II R.C. Arkin. Motor schema based mobile robot navigation. The International Journal of Robotics Research, 8(4):92–112, 1989. R.A. Brooks. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1):14–23, 1986. G. Casalino and A. Turetta. Coordination and control of multiarm, nonholonomic mobile manipulators. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2203–2210, Las Vegas, NE, Oct. 2003. P. Chiacchio, S. Chiaverini, L. Sciavicco, and B. Siciliano. Closed-loop inverse kinematics schemes for constrained redundant manipulators with task space augmentation and task priority strategy. The International Journal Robotics Research, 10(4):410–425, 1991. Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography III S. Chiaverini. Singularity-robust task-priority redundancy resolution for real-time kinematic control of robot manipulators. IEEE Transactions on Robotics and Automation, 13(3):398–410, 1997. S. Chiaverini, G. Oriolo, and I. D. Walker. Springer Handbook of Robotics, chapter Kinematically Redundant Manipulators, pages 245–268. B. Siciliano, O. Khatib, (Eds.), Springer-Verlag, Heidelberg, D, 2008. A. Escande, N. Mansard, and P.-B. Wieber. Hierarchical quadratic programming: Fast online humanoid-robot motion generation. International Journal of Robotics Research, 2013. Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography IV T.I. Fossen. Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics, Trondheim, Norway, 2002. J. Han and W.K. Chung. Coordinated motion control of underwater vehicle-manipulator system with minimizing restoring moments. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 3158–3163. IEEE, 2008. M. Hildebrandt, L. Christensen, J. Kerdels, J. Albiez, and F. Kirchner. Realtime motion compensation for ROV-based tele-operated underwater manipulators. In IEEE OCEANS 2009-Europe, pages 1–6, 2009. Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography V J. Kim, G. Marani, WK Chung, and J. Yuh. Kinematic singularity avoidance for autonomous manipulation in underwater. Proceedings of PACOMS, 2002. G. Marani, S.K. Choi, and J. Yuh. Real-time center of buoyancy identification for optimal hovering in autonomous underwater intervention. Intelligent Service Robotics, 3(3):175–182, 2010. T.W. McLain, S.M. Rock, and M.J. Lee. Coordinated control of an underwater robotic system. In Video Proceedings of the 1996 IEEE International Conference on Robotics and Automation, pages 4606–4613, 1996a. T.W. McLain, S.M. Rock, and M.J. Lee. Experiments in the coordinated control of an underwater arm/vehicle system. Autonomous robots, 3(2):213–232, 1996b. Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography VI R. Mebarki and V. Lippiello. Image-based control for aerial manipulation. Asian Journal of Control, in press, 2014. R. Mebarki, V. Lippiello, and B. Siciliano. Exploiting image moments for aerial manipulation control. In ASME Dynamic Systems and Control Conference, Palo Alto, CA, USA, 2013. N. Sarkar and T.K. Podder. Coordinated motion planning and control of autonomous underwater vehicle-manipulator systems subject to drag optimization. Oceanic Engineering, IEEE Journal of, 26(2):228–239, 2001. I. Schjølberg and T. Fossen. Modelling and control of underwater vehicle-manipulator systems. In in Proc. 3rd Conf. on Marine Craft maneuvering and control, pages 45–57, Southampton, UK, 1994. Gianluca Antonelli Biograd Na Moru, 8 October 2015
Bibliography VII B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo. Robotics: modelling, planning and control. Springer Verlag, 2009. E. Simetti, G. Casalino, S. Torelli, A. Sperind´e, and A. Turetta. Floating underwater manipulation: Developed control methodology and experimental validation within the TRIDENT project. Journal of Field Robotics, 31(3):364–385, 2013. Gianluca Antonelli Biograd Na Moru, 8 October 2015

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Underwater manipulation

  • 1.
    Underwater manipulation Gianluca Antonelli Universit`adi Cassino & ISME antonelli@unicas.it http://webuser.unicas.it/lai/robotica http://www.isme.unige.it http://www.eng.docente.unicas.it/gianluca antonelli Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 2.
    ISME in brief ItalianJoint Research Unit established in 1999 Sites: Ancona Cassino Firenze Genova Lecce Pisa Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 3.
    ISME in brief SEALab Joint Italian Navy/ISME located in La Spezia No need of advance area clearance Availability of Navy support personnel Some restrictions (activities/personnel to be listed in advance, no working at nights. . . ) Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 4.
    ISME in brief Aselected map of projects logos. . . Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 5.
    Marine Autonomous Roboticsfor InterventionS PRIN2010-2011 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 6.
    Marine Autonomous Roboticsfor InterventionS PRIN2010-2011 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 7.
    Effective Dexterous ROVOperations in Presence of Communications Latencies H2020-BG-2014 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 8.
    Effective Dexterous ROVOperations in Presence of Communications Latencies H2020-BG-2014 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 9.
    Robotic subsea explorationtechnologies H2020-SC5-2014 mineral and raw material exploration and recovery (in negotiation) Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 10.
    Outline Motivation Inverse Kinematics A possiblekinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 11.
    Applications Where uw manipulationis used/needed: Oil & gas industry Renewable energy Power/communication cables Fisheries & aquaculture Archaeology Security Natural science/biology Decommissioning Diver assistance Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 12.
    State of theart aged approach off-shore operator acts on the vehicle off-shore operator acts on the arm motors (!) voice coordination between the two manned visual feedback Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 13.
    State of theart Recent approach vehicle in automatic station keeping or docked off-shore operator with a master/slave architecture Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 14.
    State of theart Effort for working class vehicles 13 peoples on 24 hours 4 to 6 weeks ROV crew work 12 hours a day - 7/7 1 day of operation costs 100 ÷ 300 ke Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 15.
    Objective Autonomously (as muchass possible. . . ) achieve complex operations Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 16.
    Space, aerial andunderwater vehicle-manipulators DLR Canadian Space Agency ALIVE normal robots but floating base kinematic coupling dynamic coupling unstructured environment Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 17.
    Floating robots kinematics Oi η1 ηee ❅❅❘ end-effectorvelocities ❍❍ ❍❍ ❍❍ ❍❍ ❍❍ ❍❍❥ Jacobian system velocities˙ηee = ˙ηee1 ˙ηee2 = J(RI B, q)ζ ζ =   ν1 ν2 ˙q   Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 18.
    UVMS dynamics inmatrix form M(q)˙ζ + C(q, ζ)ζ + D(q, ζ)ζ + g(q, RI B) = τ formally equal to a ground-fixed industrial manipulator 1 however. . . Model knowledge Bandwidth of the sensor’s readings Vehicle hovering control Dynamic coupling between vehicle and manipulator External disturbances (current) Kinematic redundancy of the system 1 [Siciliano et al.(2009)Siciliano, Sciavicco, Villani, and Oriolo] [Fossen(2002)] [Schjølberg and Fossen(1994)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 19.
    Dynamics Movement of vehicleand manipulator coupled movement of the vehicle carrying the manipulator law of conservation of momentum Need to coordinate at velocity level ⇒ kinematic control at torque level ⇒ dynamic control 2 2 [McLain et al.(1996b)McLain, Rock, and Lee] [McLain et al.(1996a)McLain, Rock, and Lee] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 20.
    A first solution Assumingthe vehicle in hovering is not the best strategy to e.e. fine positioning3, better to kinematically compensate with the manipulator 3 [Hildebrandt et al.(2009)Hildebrandt, Christensen, Kerdels, Albiez, and Kirchner] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 21.
    Outline Motivation Inverse Kinematics A possiblekinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 22.
    Kinematic control scheme second.tasks ηd, qd τ η, q IK main task Control Output of IK (Inverse Kinematics) is the position/velocity to be controlled by the actuators (vehicle thrusters and joints’ torques) Btw, torque level usually not available ⇒ kinematic controller Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 23.
    Kinematic control inpills ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ Starting from a generic m-dimensional task (e.g., the e.e. position) σ = f(η, q) ∈ Rm ˙σ = J(η, q)ζ An inverse mapping is required Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 24.
    Kinematic control inpills ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ ✖✕ ✗✔ ■ Starting from a generic m-dimensional task (e.g., the e.e. position) σ = f(η, q) ∈ Rm ˙σ = J(η, q)ζ An inverse mapping is required Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 25.
    Kinematic control inpills A robotic system is kinematically redundant when it possesses more degrees of freedom than those required to execute a given task Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 26.
    Kinematic control inpills A robotic system is kinematically redundant when it possesses more degrees of freedom than those required to execute a given task Redundancy may be used to add additional tasks ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ ✖✕ ✗✔ ■ Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 27.
    Kinematic control inpills A robotic system is kinematically redundant when it possesses more degrees of freedom than those required to execute a given task Redundancy may be used to add additional tasks ✛ ✚ ✘ ✙ ζ ❘ ✛ ✚ ✘ ✙ ˙σ ✖✕ ✗✔ ■ ˙σa ✚✙ ✛✘ ˙σb ✙ ✖✕ ✗✔ ✶ Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 28.
    Kinematic control inpills Classical example: control e.e. position while reconfiguring the structure with internal motion Kuka Iiwa Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 29.
    Kinematic control inpills In the redundant case, the equation ˙σ = Jζ is solved by ζ = JT JJT −1 J† ˙σ + I − J† J N ζo i.e., by a pseudoinverse and an arbitrary vector projected onto the null-space need for closed-loop also. . . Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 30.
    Handling several tasks ExtendedJacobian4 Add additional (6 + n) − m constraints h(η, q) = 0 with associated Jh such that the problem is squared with ˙σ 0 = J Jh ζ 4 [Chiaverini et al.(2008)Chiaverini, Oriolo, and Walker] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 31.
    Handling several tasks AugmentedJacobian An additional task is given σh = h(η, q) with associated Jh such that the problem is squared with ˙σ ˙σh = J Jh ζ Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 32.
    Handling several tasks Taskpriority redundancy resolution σh = h(η, q) with associated Jh further projected on the the null space of the higher priority one ζ = J† ˙σ + Jh I − J† J † ˙σh − JhJ† ˙σ Also known as the exact solution with close similarities to the convex-optimization-based methods Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 33.
    Handling several tasks Singularityrobust task priority redundancy resolution 5 σh = h(η, q) with associated Jh further projected on the the null space of the higher priority one ζ = J† ˙σ + I − J† J J† h ˙σh 5 algorithmic singularities here. . . [Chiaverini(1997)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 34.
    Handling several tasks Behavioralalgorithms (behavior=task), bioinspired, artificial potentials, neuro-fuzzy, cognitive approaches, etc. btw. . . mood ? Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 35.
    Geometrical meaning ofthe null-space ˙σ = J ˙ζ with m = 1 and n = 2 is a line! (left) Range of the pseudoinverse and the null spaces are orthogonal (right) Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 36.
    Comparison between exactand robust solutions ζ = J† ˙σ + J h I − J† J † ˙σ h − J hJ† ˙σ ζ = J† ˙σ + I − J† J J† h ˙σ h Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 37.
    Comparison between exactand robust solutions ζ = J† ˙σ + J h I − J† J † ˙σ h − J hJ† ˙σ ζ = J† ˙σ + I − J† J J† h ˙σ h Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 38.
    Comparison between exactand robust solutions ζ = J† ˙σ + J h I − J† J † ˙σ h − J hJ† ˙σ ζ = J† ˙σ + I − J† J J† h ˙σ h Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 39.
    Some issues Kinematic singularities Dampedleast square Singular-value-decomposition-based filtering Other kind of filtering Algorithmic singularities Two different-priority tasks are achievable alone but not together: ranks of both J and Jh is full but not of J Jh (still the inversion of a singular matrix) Set-based/inequality control 6 Task transition vs continuity/priority 6 [Escande et al.(2013)Escande, Mansard, and Wieber, Simetti et al.(2013)Simetti, Casalino, Torelli, Sperind´e, and Turetta, Antonelli et al.(2015)Antonelli, Moe, and Pettersen] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 40.
    But. . . Whatare these tasks we are talking about ? Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 41.
    Tasks to becontrolled Given 6 + n DOFs and m-dimensional tasks: End-effector position, m = 3 pos./orientation, m = 6 distance from a target, m = 1 alignment with the line of sight, m = 2 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 42.
    Tasks to becontrolled Manipulator joint-limits several approaches proposed, m = 1 to n, e.g. h(q) = n i=1 1 ci qi,max − qi,min (qi,max − qi)(qi − qi,min) Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 43.
    Tasks to becontrolled Drag minimization, m = 1 7 h(q) = DT (q, ζ)W D(q, ζ) within a second order solution ˙ζ = J† ¨σ − ˙Jζ − k I − J† J ∂h ∂η ∂h ∂q + ∂h ∂ζ 7 [Sarkar and Podder(2001)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 44.
    Tasks to becontrolled Manipulability/singularity, m = 1 h(q) = det JJT (In 8 priorities dynamically swapped between singularity and e.e.) joints inhibited direction singularity singularity setclose to 8 [Kim et al.(2002)Kim, Marani, Chung, and Yuh, Casalino and Turetta(2003)] [Chiacchio et al.(1991)Chiacchio, Chiaverini, Sciavicco, and Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 45.
    Tasks to becontrolled Restoring moments: m = 3 keep close gravity-buoyancy of the overall system 9 m = 2 align gravity and buoyancy (SAUVIM is 4 tons) 10 fb fg τ 2 9 [Han and Chung(2008)] 10 [Marani et al.(2010)Marani, Choi, and Yuh] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 46.
    Tasks to becontrolled Obstacle avoidance m = 1 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 47.
    Tasks to becontrolled Workspace-related variables Vehicle distance from the bottom, m = 1 Vehicle distance from the target, m = 1 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 48.
    Tasks to becontrolled Sensors configuration variables Vehicle roll and pitch, m = 2 Misalignment between the camera optical axis and the target line of sight, m = 2 Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 49.
    Tasks to becontrolled Visual servoing variables Features in the image plane 11 11 [Mebarki et al.(2013)Mebarki, Lippiello, and Siciliano, Mebarki and Lippiello(in press, 2014)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 50.
    Outline Motivation Inverse Kinematics A possiblekinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 51.
    Behavioral control inpills Inspired from animal behavior sensors behavior a actuators behavior b actuators behavior c actuators How to combine them in one single behavior? Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 52.
    Behavioral control inpills Inspired from animal behavior sensors behavior a actuators behavior b actuators behavior c actuators How to combine them in one single behavior? Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 53.
    Competitive behavioral control Behaviorsare in competitions and the higher priority can subsume the lower ones12 sensors behavior b ζ2 behavior a ζ1 behavior c ζ3 ζd 12 [Brooks(1986)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 54.
    Cooperative behavioral control Behaviorscooperate and the priority is embedded in the gains13 sensors behavior b ζ2 α2 behavior a ζ1 supervisor α1 behavior c ζ3 α3 ζd 13 [Arkin(1989)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 55.
    Competitive-cooperative and tasksconflicting Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 56.
    NSB Null Space-based Behavioralcontrol Each action is decomposed in elementary behaviors/tasks motion reference command for each task ζd = J† ˙σd + Λσ σ = σd−σ Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 57.
    NSB: Merging differenttasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + J† b ˙σb,d + Λbσb primary secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 58.
    NSB: Merging differenttasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + I − J† aJa J† b ˙σb,d + Λbσb primary null space secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 59.
    NSB: Merging differenttasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + I − J† aJa J† b ˙σb,d + Λbσb primary null space secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 60.
    NSB: Merging differenttasks NSB inherits the approach of the singularity-robust task priority inverse kinematics technique ζd = J† a ˙σa,d + Λaσa + I − J† aJa J† b ˙σb,d + Λbσb primary null space secondary Thus, defining: ζa = J† a ˙σa,d + Λaσa Na = I − J† aJa ζb = J† b ˙σb,d + Λbσb ζd = ζa + Naζb Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 61.
    NSB: Three-task example ζa= J† a ˙σa,d + Λaσ1 ζb = J† b ˙σb,d + Λbσ2 ζc = J† c ˙σc,d + Λcσ3 Successive projection approach Na = I − J† aJa Nb = I − J† bJb ζd = ζa + Naζb + NaNbζc Augmented projection approach Jab = Ja Jb Nab = In − J† abJab ζd = ζa + Naζb+Nabζc Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 62.
    NSB: Three-task example ζa= J† a ˙σa,d + Λaσ1 ζb = J† b ˙σb,d + Λbσ2 ζc = J† c ˙σc,d + Λcσ3 Successive projection approach Na = I − J† aJa Nb = I − J† bJb ζd = ζa + Naζb + NaNbζc Augmented projection approach Jab = Ja Jb Nab = In − J† abJab ζd = ζa + Naζb+Nabζc Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 63.
    NSB: Three-task example ζa= J† a ˙σa,d + Λaσ1 ζb = J† b ˙σb,d + Λbσ2 ζc = J† c ˙σc,d + Λcσ3 Successive projection approach Na = I − J† aJa Nb = I − J† bJb ζd = ζa + Naζb + NaNbζc Augmented projection approach Jab = Ja Jb Nab = In − J† abJab ζd = ζa + Naζb+Nabζc Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 64.
    From behaviors toactions sensing/perception elementary behaviors actions commands supervisor Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 65.
    Simple comparison: moveto goal with obstacle avoidance obstacle avoidance σ1 = p − po ∈ R σ1,d = d J1 = ˆrT ∈ R1×2 ˆr = p − po p − po ζ1 = J† 1λ1 (d − p−po ) N(J1) = I − J† 1J1 = I − ˆrˆrT move to goal σ2 = p ∈ R2 σ2,d = pg J2 = I ∈ R2×2 ζ2 = Λ2 pg − p Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 66.
    Simple comparison: competitiveapproach Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 67.
    Simple comparison: competitiveapproach ❆ ❆ ❆ ❆ ❆❯ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 68.
    Simple comparison: competitiveapproach ❄ only obstacle avoidance Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 69.
    Simple comparison: competitiveapproach ❄ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 70.
    Simple comparison: cooperativeapproach Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 71.
    Simple comparison: cooperativeapproach ❆ ❆ ❆ ❆❯ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 72.
    Simple comparison: cooperativeapproach ❇ ❇ ❇❇◆ linear combination: higher task is corrupted Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 73.
    Simple comparison: cooperativeapproach ❄ only move-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 74.
    Simple comparison: NSB GianlucaAntonelli Biograd Na Moru, 8 October 2015
  • 75.
    Simple comparison: NSB ❆ ❆ ❆ ❆❯ onlymove-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 76.
    Simple comparison: NSB ❇ ❇ ❇◆ null-space-projection:higher task is fulfilled Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 77.
    Simple comparison: NSB ❄ onlymove-to-goal Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 78.
    Gain tuning Cooperative task ab c situation 1 α1,1 α1,2 α1,3 sit. 2 α2,1 α2,2 α2,3 sit. 3 α3,1 α3,2 α3,3 sit. 4 α4,1 α4,2 α4,3 NSB Each behavior tuned as if it was alone but in each situation the priority needs to be designed Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 79.
    Gain tuning Cooperative task ab c d situation 1 α1,1 α1,2 α1,3 α1,4 sit. 2 α2,1 α2,2 α2,3 α2,4 sit. 3 α3,1 α3,2 α3,3 α3,4 sit. 4 α4,1 α4,2 α4,3 α4,4 NSB Each behavior tuned as if it was alone but in each situation the priority needs to be designed Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 80.
    Gain tuning Cooperative task ab c situation 1 α1,1 α1,2 α1,3 sit. 2 α2,1 α2,2 α2,3 sit. 3 α3,1 α3,2 α3,3 sit. 4 α4,1 α4,2 α4,3 NSB Each behavior tuned as if it was alone but in each situation the priority needs to be designed Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 81.
    Stability analysis Lyapunov function14 V(˜σ) = 1 2 ˜σT ˜σ > 0 where ˜σ = ˜σT a ˜σT b ˜σT c T ˙V = −˜σT   Ja Jb Jc   v = −˜σT M ˜σ = −˜σT       Λa Oma,mb Oma,mc JbJ† aΛa JbNaJ† bΛb JbNJ† cΛc JcJ† aΛa JcNaJ† bΛb JcNJ† cΛc       ˜σ ˙V < 0 depending on the mutual relationships among the Jacobians 14 [Antonelli(2009)] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 82.
  • 83.
    Outline Motivation Inverse Kinematics A possiblekinematic solution: NSB behavioral control Simulation/experiments Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 84.
    Numerical simulation onMARIS model: underwater 6-DOF vehicle + 7-DOF manipulator Reach a pre-grasp configuration in terms of end-effector position and orientation priority-1 task: e.e. configuration (m = 6) priority-2 task: vehicle roll+pitch (m = 2) priority-3 task: position of joint 2 (m = 1) only e.e. ⇒ complete solution ⇒ Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 85.
    Numerical simulation onMARIS model: underwater 6-DOF vehicle + 7-DOF manipulator Cameraman action: keep the object in the field of view priority-1 task: field of view (m = 2) priority-2 task: vehicle roll+pitch (m = 2) priority-3 task: arm manipulability (m = 1) priority-4 task: mechanical joint limits (m = 7) animation ⇒ Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 86.
    Simulations and experimentswithin TRIDENT [Simetti et al.(2013)Simetti, Casalino, Torelli, Sperind´e, and Turetta] Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 87.
    Numerical simulation onMARIS model: interaction within the task-priority approach An impedance external loop is designed to push a button Σ0 ΣI Σee Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 88.
    Numerical simulation onMARIS model: interaction within the task-priority approach An impedance external loop is designed to turn a valve Σ0 ΣI Σee have a look at the experiments made by Pedro Sanz, Pere Ridao and colleagues within TRIDENT Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 89.
    The presented resultsare the outcome of the work of several colleagues from the University of Cassino, the Consortium ISME and PRISMA, the projects DEXROV and MARIS Filippo Arrichiello, Elisabetta Cataldi, Stefano Chiaverini, Paolo Di Lillo ISME PRISMA Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 90.
    Bibliography I G. Antonelli. Stabilityanalysis for prioritized closed-loop inverse kinematic algorithms for redundant robotic systems. IEEE Transactions on Robotics, 25(5):985–994, October 2009. G. Antonelli. Underwater robots. Springer Tracts in Advanced Robotics, Springer-Verlag, Heidelberg, D, 3rd edition, January 2014. G. Antonelli, S. Moe, and K. Pettersen. Incorporating set-based control within the singularity-robust multiple task-priority inverse kinematics. In 23th Mediterranean Conference on Control and Automation, pages 1132–1137, Torremolinos, S, June 2015. Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 91.
    Bibliography II R.C. Arkin. Motorschema based mobile robot navigation. The International Journal of Robotics Research, 8(4):92–112, 1989. R.A. Brooks. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1):14–23, 1986. G. Casalino and A. Turetta. Coordination and control of multiarm, nonholonomic mobile manipulators. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2203–2210, Las Vegas, NE, Oct. 2003. P. Chiacchio, S. Chiaverini, L. Sciavicco, and B. Siciliano. Closed-loop inverse kinematics schemes for constrained redundant manipulators with task space augmentation and task priority strategy. The International Journal Robotics Research, 10(4):410–425, 1991. Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 92.
    Bibliography III S. Chiaverini. Singularity-robusttask-priority redundancy resolution for real-time kinematic control of robot manipulators. IEEE Transactions on Robotics and Automation, 13(3):398–410, 1997. S. Chiaverini, G. Oriolo, and I. D. Walker. Springer Handbook of Robotics, chapter Kinematically Redundant Manipulators, pages 245–268. B. Siciliano, O. Khatib, (Eds.), Springer-Verlag, Heidelberg, D, 2008. A. Escande, N. Mansard, and P.-B. Wieber. Hierarchical quadratic programming: Fast online humanoid-robot motion generation. International Journal of Robotics Research, 2013. Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 93.
    Bibliography IV T.I. Fossen. MarineControl Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics, Trondheim, Norway, 2002. J. Han and W.K. Chung. Coordinated motion control of underwater vehicle-manipulator system with minimizing restoring moments. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 3158–3163. IEEE, 2008. M. Hildebrandt, L. Christensen, J. Kerdels, J. Albiez, and F. Kirchner. Realtime motion compensation for ROV-based tele-operated underwater manipulators. In IEEE OCEANS 2009-Europe, pages 1–6, 2009. Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 94.
    Bibliography V J. Kim,G. Marani, WK Chung, and J. Yuh. Kinematic singularity avoidance for autonomous manipulation in underwater. Proceedings of PACOMS, 2002. G. Marani, S.K. Choi, and J. Yuh. Real-time center of buoyancy identification for optimal hovering in autonomous underwater intervention. Intelligent Service Robotics, 3(3):175–182, 2010. T.W. McLain, S.M. Rock, and M.J. Lee. Coordinated control of an underwater robotic system. In Video Proceedings of the 1996 IEEE International Conference on Robotics and Automation, pages 4606–4613, 1996a. T.W. McLain, S.M. Rock, and M.J. Lee. Experiments in the coordinated control of an underwater arm/vehicle system. Autonomous robots, 3(2):213–232, 1996b. Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 95.
    Bibliography VI R. Mebarkiand V. Lippiello. Image-based control for aerial manipulation. Asian Journal of Control, in press, 2014. R. Mebarki, V. Lippiello, and B. Siciliano. Exploiting image moments for aerial manipulation control. In ASME Dynamic Systems and Control Conference, Palo Alto, CA, USA, 2013. N. Sarkar and T.K. Podder. Coordinated motion planning and control of autonomous underwater vehicle-manipulator systems subject to drag optimization. Oceanic Engineering, IEEE Journal of, 26(2):228–239, 2001. I. Schjølberg and T. Fossen. Modelling and control of underwater vehicle-manipulator systems. In in Proc. 3rd Conf. on Marine Craft maneuvering and control, pages 45–57, Southampton, UK, 1994. Gianluca Antonelli Biograd Na Moru, 8 October 2015
  • 96.
    Bibliography VII B. Siciliano,L. Sciavicco, L. Villani, and G. Oriolo. Robotics: modelling, planning and control. Springer Verlag, 2009. E. Simetti, G. Casalino, S. Torelli, A. Sperind´e, and A. Turetta. Floating underwater manipulation: Developed control methodology and experimental validation within the TRIDENT project. Journal of Field Robotics, 31(3):364–385, 2013. Gianluca Antonelli Biograd Na Moru, 8 October 2015