Underwater Robots - Gianluca Antonelli.pdf

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1.1 Underwater Vehicles 3 This objective ischallenging from the technological as well as from the theoretical aspects since it implies awide range of technical and research topics. Sending anautonomous vehicle in an unknown and unstructured environment, with limited on-line communication, requires some on board intelligence and the ability ofthe vehicle to react in areliable way to unexpected situations. The sensory system of the vehicle must deal with anoisy and unstructured environment; moreover, technologies asGPS (Global Positioning System) are not applicable due to the impossibility tounderwater electromagnetic transmission at GPS specific frequencies; vision based systems are not fully reliable due tothe generally poor visibility. The actuating system is usually composed of thrusters and control surfaces, both of them have a non-linear dynamics and are strongly affected by the hydrodynamic effects. The book of T. Fossen [127] is one of the first books dedicated tocontrol problems of marine systems, the case of surface vehicles, in fact, isalso taken into account. The same author presents, in [128], an updated and extended version of the topics developed in the first book. Some very interesting talks about state of the art and direction of the underwater robotics were discussed by, e.g., J. Yuhin[315, 317], J. Yuhand M. West in [321], T. Ura in [292]. At the best of our knowledge this is the sole book dedicated tocontrol problems of underwater robotic systems with particular regard with respect to the manipulation [8]; this is an emerging topic in which solid experimental results still need to be achieved. In this chapter anoverview of control problem inunderwater robotics is presented; some of these aspects will be further analyzed along this book. 1.1 Underwater Vehicles The therm Remotely Operated Vehicle (ROV) denotes an underwater vehicle physically linked, via the tether, toanoperator that can be on asubmarine or on asurface ship. The tether isincharge ofgiving power tothe vehicle as well as closing the manned control loop. Autonomous Underwater Vehicles (AUVs), on the other side, are supposed to be completely autonomous, thus relying toonboard power system and intelligence .These two types ofunderwater vehicles share some control problems, inthis case one has to refer to them as Unmanned Underwater Vehicles (UUVs). In case of missions that require interaction with the environment, the vehicle can beequipped with one or more manipulators; in this case the system is usually called Underwater Vehicle-Manipulator System (UVMS). Referring toAUVs, [294, 321] present the state of the art of several existing AUVs and their control architectures. Currently, there are about 100 prototypes in the laboratories all over the world, see e.g., [321]. Among the others: r2D4 developed at URA laboratory of the University of Tokyo (Tokyo, Japan, http://underwater.iis.u-tokyo.ac.jp ), ABE of the Deep Submerge Laboratory of the Woods Hole Oceanographic Institution
4 1. Introduction (Massachusetts, USA, http://www.dsl.whoi.edu ), Odissey IId belonging to the AUV Laboratory ofthe Massachusetts Institute of Tec hnology (Massachusetts, USA, http://auvlab.mit.edu ), ODIN III designed at the Autonomous Systems Laboratory of the University ofHawaii (Hawaii, USA, http://www.eng.hawaii.edu/∼ asl), Phoenix and ARIES, torpedo-like vehicles developed at the Naval Postgraduate School (California, USA, http://www.cs.nps.navy.mil/research/auv/). Currently, very few companies sell AUVs; among the others: Bluefin Corporations ( http://www.bluefinrobotics.com) developed, in collaboration with MIT, different AUVs, such asBluefin 21, for deep operations up to 4500 m; C&C technologies ( www.cctechnol.com) designed Hugin 3000, able to run autonomously for up to 50 h; the Canadian ISE Research Ltd ( http://www.ise.bc.ca) developed several AUVs such as, e.g, Explorer or Theseus; Hafmynd, inIceland, designed avery small AUV named Gavia ( http://www.gavia.is); the Danish Maridan (http://www.maridan.dk) developed the Maridan 600 vehicle. Fig. 1.3. Sketch ofthe underwater vehicle-manipulator system SAUVIM, currently under development atthe Autonomous Systems Laboratory of the University of Hawaii (courtesy ofJ.Yuh)
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: 2 1. Introduction Maybe the first i
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 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
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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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