Extending persistent monitoring by combining ocean models and Markov decision processes

Al-Sabban, Wesam H., Gonzalez, Luis F., & Smith, Ryan N. (2012) Extending persistent monitoring by combining ocean models and Markov decision processes. In Proceedings of the 2012 MTS/IEEE Oceans Conference, Hampton Roads, Virginia.

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Ocean processes are complex and have high variability in both time and space. Thus, ocean scientists must collect data over long time periods to obtain a synoptic view of ocean processes and resolve their spatiotemporal variability. One way to perform these persistent observations is to utilise an autonomous vehicle that can remain on deployment for long time periods. However, such vehicles are generally underactuated and slow moving. A challenge for persistent monitoring with these vehicles is dealing with currents while executing a prescribed path or mission. Here we present a path planning method for persistent monitoring that exploits ocean currents to increase navigational accuracy and reduce energy consumption.

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ID Code: 51033
Item Type: Conference Paper
Refereed: Yes
Keywords: Markov decision processes, autonomous underwater vehicles, ocean model, persistent monitoring, path planning
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MARITIME ENGINEERING (091100) > Special Vehicles (091106)
Divisions: Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Please consult the authors.
Deposited On: 25 Jun 2012 01:21
Last Modified: 15 Jul 2017 21:01

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