Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Advanced Manipulation for Underwater Sampling

  • Giuseppe Casalino
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5102-9_129-1


This entry deals with the kinematic self-coordination aspects to be managed by part of underwater floating manipulators, whenever employed for sample collections at the seafloor.Kinematic self-coordination is here intended as the autonomous ability exhibited by the system in closed loop specifying the most appropriate reference velocities for its main constitutive parts (i.e., the supporting vehicle and the arm) in order to execute the sample collection within the respect of both safety and best operability conditions for the system while also guaranteeing the needed “execution agility” in performing the task, particularly useful in case of underwater repeated collections. To this respect, the devising and employment of unifying control framework capable of guaranteeing the above properties will be outlined.Such framework is however intended only representing the so-called Kinematic Control Layer (KCC), upper-lying a Dynamic Control Layer (DCL), where the overall system dynamic and hydrodynamic effects are suitably accounted to the benefit of closed loop tracking the reference system velocities closed loop provided by the KLC itself. Since the DCL design be carried out in a way which is substantially independent from the system mission (s), it will not constitute a specific topic of this entry, even if some orienting references about it will be provided.At this entry’s end, as a follow-up of the resulting structural invariance of the devised KCL framework, future challenges addressing much wider and complex underwater applications will be foreseen, beyond the here-considered sample collection one.


Underwater Vehicle Stereo Camera Cartesian Space System Velocity Equality Objective 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.University of GenoaGenoaItaly