Underwater Robots - Gianluca Antonelli.pdf

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1.3 Actuation 5 The UVMSs are still under development; several laboratories built some manipulation devices on underwater structures but very few of them can be considered as capable of autonomous manipulation. SAUVIM (see Figure 1.3), asemi-autonomous vehicle with an Ansaldo 7-link manipulator is under development at the Autonomous Systems Laboratory of the University of Hawaii; this vehicle, inthe final version, will be able to operate at the depth of4000 ts m; preliminary experiments were performed. AMADEUS, an acronym for Advanced MAnipulation for DEep Underwater Sampling, funded by the European Commission, that involved the Heriot-Watt University (UK), the Università diGenova (Italy), the National Research Council-ISSIA (Italy), the Universitat de Barcelona (Spain), the Institute of Marine Biology of Crete (Greece). The project focused on the coordinated control of two teleoperated underwater Ansaldo 7-link manipulators and the development of an underwater hand equipped with aslip sensor; Figure 1.4 shows awet test in apool. The French company Cyberné tix ( http://www.cybernetix.fr) sells hydraulic manipulators mounted onROVs that can be remotely operated by means of ajoystick orinamaster-slave configuration. 1.2 Sensorial Systems The AUVs need to operate in an unstructured hazardous environment; one of the major problems with underwater robotics is in the localization task due to the absence of asingle, proprioceptive sensor that measures the vehicle position and the impossibility touse the GPS under the water. The use of redundant multi-sensor system, thus, is common inorder to perform sensor fusion tasks and give fault detection and tolerance capability tothe vehicle. To give anidea of the sensors used in underwater robotics, Table 1.1 lists the sensors and the corresponding measured variable for Unmanned Underwater Vehicles (UUVs). As an example, Table 1.2 reports some data of the instrumentations of the ROV developed at the John Hopkins University [271] and Table 1.3 some data of the AUV ODIN III [81, 323]. 1.3 Actuation Underwater vehicles are usually controlled by thrusters and/or control surfaces. Control surfaces, such asrudders and sterns, are common incruise vehicles; those are torpedo-shaped and usually used in cable/pipeline inspection. The main configuration isnot changed in the last century, there is a main thruster and at least one rudder and one stern, inFigure 1.5 it is reported the underwater manned vehicle named SLC ( Siluro aLenta Corsa, Slow RunningTorpedo), or maiale (pig),used in the second world warbythe Regia
6 1. Introduction Fig. 1.4. Coordinated control of two seven-link Ansaldo manipulators during a wet test in a pool (courtesy of G. Casalino, Genoa Robotics And Automation Laboratory, Università diGenova and G. Veruggio, National Research Council- ISSIA, Italy) Table 1.1. UUV possible instrumentation sensor measured variable Inertial System linear acceleration and angular velocity Pressure-meter vehicle depth Frontal sonar distance from obstacles Vertical sonar distance from the bottom Ground Speed sonar relative velocity vehicle/bottom Current-meter relative velocity vehicle/current Global Positioning System absolute position at the surface Compass orientation Acoustic baseline absolute position in known area Vision systems relative position/velocity Acoustic Doppler Current Profiler water current atseveral positions
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: 4 1. Introduction (Massachusetts, U
Page 31 and 32: 8 1. Introduction Fig. 1.5. Pig, ma
Page 33 and 34: 10 1. Introduction An example of cr
Page 35 and 36: 12 1. Introduction Fig. 1.8. Romeo
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.
Page 69 and 70: 3.2 Earth-Fixed-Frame-Based, Model-
Page 71 and 72: o x z o b z b x b 3.3 Earth-Fixed-F
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Page 75 and 76: o o b y x y b x b 3.5 Model-Based C
Page 77 and 78: 3.6 Mixed Earth/Vehicle-Fixed-Frame
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3.7 Jacobian-Transpose-Based Contro
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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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