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| Content Provider | ACM Digital Library |
|---|---|
| Author | Pereira, Arvind Heidarsson, Hordur Sukhatme, Gaurav S. Smith, Ryan N. Das, Jnaneshwar |
| Abstract | Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography through their versatility, autonomy and endurance. However, they are still an underutilized technology. For coastal operations, the ability to track a certain feature is of interest to ocean scientists. Adaptive and predictive path planning requires frequent communication with significant data transfer. Currently, most AUVs rely on satellite phones as their primary communication. This communication protocol is expensive and slow. To reduce communication costs and provide adequate data transfer rates, we present the hardware and software components that make up a communication framework, which provides robust, disruption-tolerant, cost-effective glider operation for a coastal region. The framework is specifically designed to address multi-sensor deployments. We provide a system overview and present testing and coverage data for the network. Additionally, we include an application of ocean-model driven trajectory design, which can benefit from the use of this network and communication system. The presented combination of infrastructure, software development and deployment experience brings us closer to the goal of providing a reliable and cost-effective data transfer framework to enable real-time, optimal trajectory design, based on ocean model predictions, to gather in situ measurements of interesting and evolving ocean features and phenomena. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781605588216 |
| DOI | 10.1145/1654130.1654132 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Autonomous underwater vehicles Trajectory design Slocum glider Embedded sensor networks Freewave Implementation |
| Content Type | Text |
| Resource Type | Article |
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