Underwater Robots - Gianluca Antonelli.pdf

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1.5.1 Fault Detection/Tolerance for UUVs 1.7 Future Perspectives 11 ROVS and AUVs are complex systems engaged in missions in un-structured, unsafe environments for which the degree of autonomy becomes acrucial issue. In this sense, the capability todetect and tolerate faults is akey to successfully terminate the mission or recuperate the vehicle. Anoverview of fault detection and fault tolerance algorithms, specifically designed for UUVs is presented in Chapter 4. In Figure 1.8, the vehicle Romeo operating over thermal vents inthe Milos Island, Aegean Sea, Greece, is shown; this vehicle has been built at the RobotLab, National Research Council (CNR-ISSIA), Genova, Italy ( http://www.robotlab.ian.ge.cnr.it/). This vehicle has been object of several experimental studies on fault detection/tolerance algorithms. 1.6 UVMS’ Coordinated Control The use of Autonomous <strong>Underwater</strong> Vehicles (AUVs) equipped with amanipulator (UVMS) to perform complex underwater tasks give rise to challenging control problems involving nonlinear, coupled, and high-dimensional systems. Currently, the state of the art is represented by tele-operated master/slave architectures; few research centers are equipped with autonomous systems [180, 321]. The core of this monograph isdedicated to this topic, in Chapter 6the kinematic control will be discussed, Chapter 7presents dynamic control laws for UVMSs and Chapter 8shows some interaction control schemes. 1.7 Future Perspectives <strong>Underwater</strong> robotics research isaninteresting topic. Current technology allows to safely run long duration missions that involve one single AUV, e.g., as in the case of the Naval Postgraduate School or the Ura laboratory vehicles, or to execute manned-in-the-loop manipulation tasks. There are, however, research topics that need to be further investigated. The UVMSs need to be studied in the field; from the theoretical aspect, in fact, many ofthe associated problems have been studied and, possibly, solved: kinematic and dynamic control laws, as well as interaction control laws have been designed and successfully simulated. Fewexperimental set-up have also been used; these, however, reproduced only oversimplified environments. Interesting results might beachieved by means of afully actuated autonomous underwater vehicle carrying a6-DOF manipulator. The actuating system might beimproved inaneffort to reduce the limit cycles caused by the thrusters’ dynamics at very low velocities; new blade
12 1. Introduction Fig. 1.8. Romeo operating over thermal vents inthe Milos Island, Aegean Sea, Greece, during the final demo of the EC-funded project ARAMIS (courtesy of M. Caccia, National Research Council-ISSIA, Italy) profiles, e.g., might bestudied in order to linearize the input-output thruster relationships. The sensory system is also still object of research; recent advances concern the possibility topractically achieve Simultaneously Localization And Mapping (SLAM) with one AUV orperform sensor fusion by the use of platoon of AUVs. In the case of SAUVIM, apassive manipulator isconsidered with 6DOFs in charge of measuring the vehicle position/orientation when the UVMS is close to astructure.
Page 1 and 2: Springer Tracts in Advanced Robotic
Page 3 and 4: Gianluca Antonelli Underwater Robot
Page 5 and 6: Editorial Advisory Board EUROPE Her
Page 7 and 8: Elalocomotiva sembrava fosse un mos
Page 9 and 10: Acknowledgements The contributions
Page 11 and 12: Preface to the Second Edition The p
Page 13 and 14: XVIII Preface to the First Edition
Page 15 and 16: XX Preface tothe First Edition mani
Page 17 and 18: XXII Notation η q =[η T 1 ε T η
Page 19 and 20: XXIV Notation ˜x error variable de
Page 21 and 22: XXVI Contents 3. Dynamic Control of
Page 23 and 24: XXVIIIContents A. Mathematical mode
Page 25 and 26: 2 1. Introduction Maybe the first i
Page 27 and 28: 4 1. Introduction (Massachusetts, U
Page 29 and 30: 6 1. Introduction Fig. 1.4. Coordin
Page 31 and 32: 8 1. Introduction Fig. 1.5. Pig, ma
Page 33: 10 1. Introduction An example of cr
Page 37 and 38: 2. Modelling ofUnderwater Robots
Page 39 and 40: 2.2 Rigid Body’s Kinematics 17 Th
Page 41 and 42: J T k,oqJ k,oq = 1 4 I 3 , that all
Page 43 and 44: 5. compute the quaternion Q by the
Page 45 and 46: 2.3 Rigid Body’s Dynamics 23 Let
Page 47 and 48: 2.4 Hydrodynamic Effects 25 τ 1 =
Page 49 and 50: C A ( ν )=− C T A ( ν ) ∀ ν
Page 51 and 52: 2.4 Hydrodynamic Effects 29 effects
Page 53 and 54: 2.5 Gravity and Buoyancy 2.5 Gravit
Page 55 and 56: 2.6 Thrusters’ Dynamics 33 Fig. 2
Page 57 and 58: 2.7 Underwater Vehicles’ Dynamics
Page 59 and 60: y z 2.8 Kinematics of Manipulators
Page 61 and 62: 2.9 Dynamics ofUnderwater Vehicle-M
Page 63 and 64: 2.9 Dynamics ofUnderwater Vehicle-M
Page 65 and 66: 2.11 Identification 43 f e = K ( x
Page 67 and 68: 3. Dynamic Control of 6-DOF AUVs 3.
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Page 77 and 78: 3.6 Mixed Earth/Vehicle-Fixed-Frame
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Page 81 and 82: 3.8 Comparison Among Controllers 59
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3.9 Numerical Comparison Among the
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force [N] moment [Nm] 20 10 0 −10
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80 4. Fault Detection/Tolerance Str
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5. Experiments of Dynamic Control o
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5.3 Experiments of Dynamic Control
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[m] [m] 5 4 3 2 1 0 0 100 200 300 4
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5.3 Experiments of Dynamic Control
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5.4 Experiments of Fault Tolerance
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[deg] 120 100 80 60 40 20 0 −20 5
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6. Kinematic Control of UVMSs “.
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6.2 Kinematic Control 107 UVMS. Thi
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6.2 Kinematic Control 109 singulari
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6.2 Kinematic Control 111 case of t
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6.5 Singularity-Robust Task Priorit
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6.6 Fuzzy Inverse Kinematics 121 It
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Simulations 6.6 Fuzzy Inverse Kinem
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6.6 Fuzzy Inverse Kinematics 125 Fi
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vehicle attitude [deg] 20 10 0 −1
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6.6 Fuzzy Inverse Kinematics 129 It
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[-] 1 0.8 0.6 0.4 0.2 0 close 0 20
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joint positions 1-3 [deg] joint pos
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e.e. position error [m] 0.02 0.01 0
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7. Dynamic Control of UVMSs 7.1 Int
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7.2 Feedforward Decoupling Control
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7.2 Feedforward Decoupling Control
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7.4 Nonlinear Control for UVMSs wit
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7.5 Non-regressor-Based Adaptive Co
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7.6 Sliding Mode Control 7.6 Slidin
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7.6 Sliding Mode Control 153 u = B
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7.6 Sliding Mode Control 155 Practi
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7.7 Adaptive Control 157 of gravity
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Let us consider the scalar function
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7.7 Adaptive Control 161 The vehicl
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[deg] 3 2.5 2 1.5 1 0.5 7.8 Output
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7.8 Output Feedback Control 165 It
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7.8 Output Feedback Control 167 whe
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7.8 Output Feedback Control 169 C T
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7.8 Output Feedback Control 171 wit
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[m] [deg] [deg] 0.1 0.05 0 −0.05
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[m] [-] [deg] x 10−3 10 8 6 4 2 0
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[Nm] [Nm] [Nm] 500 0 −500 50 0
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[m] [-] [deg] x 10−3 10 8 6 4 2 0
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[N] [N] [N] 20 0 −20 40 20 0 −2
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[Nm] [Nm] [Nm] 50 0 −50 350 300 2
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7.9 Virtual Decomposition Based Con
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joint torques [Nm] 200 0 −200 −
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e.e. orientation error [deg] 2 1 0
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vehicle position [m] 0.1 0.05 0 −
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8. Interaction Control of UVMSs 8.1
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8.3 Impedance Control 203 8.2 Dexte
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8.4 External Force Control 205 The
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8.4 External Force Control 207 be f
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8.4 External Force Control 209 that
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8.4 External Force Control 211 UVMS
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[N] [m] 350 300 250 200 150 100 50
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[deg] [deg] 10 5 0 −5 −10 50 40
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8.5 Explicit Force Control 217 wher
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8.5 Explicit Force Control 219 wher
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[m] [deg] 0.2 0.1 0 −0.1 8.5 Expl
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[m] [deg] 0.2 0.1 0 −0.1 −0.2 0
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226 9. Coordinated Control of Plato
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238 10. Concluding Remarks control
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240 A. Mathematical models ⎡ 6 .
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242 A. Mathematical models using th
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244 A. Mathematical models L = 2 m
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References 1. Alekseev Y.K., Kosten
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References 249 28. Antonelli G.and
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References 251 62. Caccia M., Indiv
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References 253 96. Cui Y. and Sarka
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References 255 134. Garcia R., Puig
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References 257 168. Kato N. and Lan
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References 259 mera. In: IEEE Inter
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References 261 235. Podder T.K. and
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References 263 273. Solvang B., Den
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References 265 310. Yoerger D.R., S
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