Sensor-Driven Online Coverage Planning for Autonomous Underwater Vehicles

At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameter...

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Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2013-12, Vol.18 (6), p.1827-1838
Main Authors: Paull, Liam, Saeedi, Sajad, Seto, Mae, Li, Howard
Format: Article
Language:English
Subjects:
Adaptive mission planning
Autonomous underwater vehicles
coverage path planning
Entropy
hardware in the loop (HWIL)
information gain
Information theory
Linear programming
Marine
Mechatronics
mine countermeasure (MCM)
On-line systems
Online
Optimization
Path planning
Robot sensing systems
Sensors
sidescan sonar (SSS)
Sonar
Surveys
Underwater vehicles
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Summary:At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multiobjective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2012.2213607