Kevin Gomes

Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned

With the advent of Autonomous Underwater Vehicles (AUVs) and other mobile platforms, marine robotics have had substantial impact on the oceanographic sciences. These systems have allowed scientists to collect data over temporal and spatial scales that would be logistically impossible or prohibitively expensive using traditional ship-based measurement techniques. Increased dependence of scientists on such robots has permeated scientific data gathering with future field campaigns involving these platforms as well as on entire infrastructure of people, processes and software, on shore and at sea. Recent field experiments carried out with a number of surface and underwater platforms give clues to how these technologies are coalescing and need to work together. We highlight one such confluence and describe a future trajectory of needs and desires for field experiments with autonomous marine robotic platforms. Our 2010 inter-disciplinary experiment in the Monterey Bay involved multiple platforms and collaborators with diverse science goals. One important goal was to enable situational awareness, planning and collaboration before, during and after this large-scale collaborative exercise. We present the overall view of the experiment and describe an important shore-side component, the Oceanographic Decision Support System (ODSS), its impact and future directions leveraging such technologies for field experiments.

MBARI Technology for Self-Configuring Interoperable Ocean Observatories

The ocean science and engineering communities have identified some key requirements for large-scale ocean observatories at a recent ORION-sponsored workshop, and these requirements are being refined by the ORION project and others. MBARI has developed and deployed hardware and software technologies that address many of these requirements. In particular, we describe how these technologies address several key issues: (1) scalable integration, configuration, and management of large numbers of diverse instruments and data streams, (2) reliable association of instrument data and contextual metadata, and (3) development of observatory infrastructure and components that are interoperable among a variety of observatory architectures, including at-sea systems with relatively limited power and bandwidth availability. We focus on three technologies developed at MBARI. These technologies work together to enable MBARIs self-configuring self-describing MOOS mooring-based observatory. Yet these technologies have been designed to be largely independent of an observatorys physical implementation, and will be deployed for testing on the MARS cable-to-shore observatory test-bed. Moreover each of the technologies provide components that could be selectively used by other observatories. For example, PUCKs could be widely useful and are not dependent in any way on SIAM middleware or SSDS metadata structures. We also describe lessons learned during development and deployment of these technologies, and how policies and human-procedures interact with the new technologies. Finally, we discuss how these technologies are being refined through community efforts such as the emerging Marine Plug and Work Consortium and Marine Metadata Initiative

MBARI's SSDS: operational, extensible data management for ocean observatories

The Monterey Bay Aquarium Research Institute (MBARI) has collected science data for 15 years from many oceanographic instruments and systems. The Monterey ocean observing system, or MOOS, presents new oceanographic data management challenges. To meet the data management requirements, MBARI is developing a flexible, extensible data management solution, the shore side data system (SSDS). This data management solution addresses the complete data life cycle, including instrument (and metadata) development, data ingest, archival, search and access, and visualization and analysis. Working with MOOS infrastructure software, the SSDS can easily support new instruments, data streams, and data sets, from all types of instruments and platforms (for example, moorings, AUVs, and ships). The current status of SSDS development will be presented, including lessons learned from work to date, and the standard tools and protocols, which have been adopted.

ODSS: A decision support system for ocean exploration

We have designed, built, tested and fielded a decision support system which provides a platform for situational awareness, planning, observation, archiving and data analysis. While still in development, our inter-disciplinary team of computer scientists, engineers, biologists and oceanographers has made extensive use of our system in at-sea experiments since 2010. The novelty of our work lies in the targeted domain, its evolving functionalities that closely tracks how ocean scientists are seeing the evolution of their own work practice, and its actual use by engineers, scientists and marine operations personnel. We describe the architectural elements and lessons learned over the more than two years use of the system.

Oceanographic Data Provenance Tracking with the Shore Side Data System

The importance of tracking the provenance of electronic data becomes apparent when data set providers need to also provide metadata describing where the data came from. This need has driven the development of a practical oceanographic data provenance system at the Monterey Bay Aquarium Research Institute. MBARIs Shore Side Data System is designed to manage data collected, processed, and archived from oceanographic observatories. We describe the provenance tracking aspects of this system and the lessons learned from its implementation in an operational environment.

Issues in Data Management in Observing Systems and Lessons Learned

With ocean observatories growing in importance and several development efforts underway, it is critical to understand the goals and issues that an ocean observing system will have to face and solve. There are many components such as network infrastructure, instrumentation, control systems, data systems and client applications that will need to interoperate seamlessly in order for the observatory to effectively meet the needs of the research community, policy makers, and the general public. For example, instruments serve as the critical foundation to the usefulness of the information extracted from an ocean observatory. However, configuring these instruments for deployment in an observatory can be a time consuming and error-prone task. Although manageable on an instrument-by-instrument basis, configuration becomes a very important issue as the size of the observatory and the number of instruments it operates grows. Each instrument type also has its unique power, communication and bandwidth requirements that further complicate their integration into observatory systems. As the complexity of integrating the instruments into the observatory increases, so does the overall operating cost of the observatory, thus affecting the overall capability of the system. For this reason, adding and removing instruments needs to be as simple as possible, which necessitates that the infrastructure handle a large portion of that integration automatically. Once these instruments are successfully deployed, the infrastructure must also be able to monitor the health and status of the various observatory assets. Once past the configuration and management issues of instruments, the observatory still faces other issues from this collection of data from these heterogeneous instruments. Metadata and data management are particularly difficult problems to handle from a systematic perspective. Being able to capture and utilize metadata and data is certainly one requirement of a data management system, but having it operate in an automated and robust way creates even more complications. The metadata associated with data must be correctly captured by the system but also maintained correctly and linked with other related metadata throughout the system. Through this metadata, the relationship between the data and its context (source, environment, location, etc) can be captured and utilized in the analysis of the data in search of various phenomenon like events, trends, patterns, etc. Adding to these complicated tasks is the requirement that this data system must interoperate with other systems as both a server and a client. Even with these high level goals and issues defined, there is no substitute for practical experience in affirming that the right goals and issues were identified. At the Monterey Bay Aquarium Research Institute (MBARI), we have had an active ocean observing development project (Monterey Ocean Observing System-MOOS) going for the past several years. With that experience, we have been able to identify several key issues and lessons learned that are relevant to ocean observatories both from the development and operational perspectives. This paper will describe the different goals that the MOOS system and its associated data management system, the Shore-Side Data System (SSDS) address. Specifically, we will discuss how the SSDS handles various data and metadata related issues and what is gained by solving those issues. Practical examples of these solutions will be given and they will be used to illustrate how, and why, certain issues are important for the data management system to address. As a final wrap up, a section on lessons learned will be discussed to help transition what we have learned to the general oceanographic community

Observatory Middleware Framework

We are designing and prototyping a generalized observatory middleware framework (OMF) to integrate existing and proposed technologies and reduce duplication of functionality across observatories. Our implementation consists of an enterprise service bus (ESB) architecture capable of integrating a wide variety of message-based technologies, a security proxy (SP) that uses X.509 credentials to sign and verify messages to and from the ESB, and an instrument proxy (IP) based on widely-accepted encoding and interface standards that provides common access to both MBARI-specific and native instruments. Our poster details the frameworks architecture and the use case scenarios for which we are designing the prototype system. It will include a discussion of our assumptions, the choices we have made architecturally, and the implementation approach for the prototype we are producing.

Observatory Middleware Framework (OMF)

Large observatory projects (such as the Large Synoptic Sky Telescope (LSST), the Ocean Observatories Initiative (OOI), the National Ecological Observatory Network (NEON), and the Water and Environmental Research System (WATERS)) are poised to provide independent, national-scale in-situ and remote sensing cyberinfrastructures to gather and publish “community”-sensed data and generate synthesized products for their respective research communities. However, because a common observatory management middleware does not yet exist, each is building its own customized mechanism to generate and publish both derived and raw data to its own constituents, resulting in inefficiency and unnecessary redundancy of effort, as well as proving problematic for the efficient aggregation of sensor data from different observatories. The Observatory Middleware Framework (OMF) presented here is a prototype of a generalized middleware framework intended to reduce duplication of functionality across observatories. OMF is currently being validated through a series of bench tests and through pilot implementations to be deployed on the Monterey Ocean Observing System (MOOS) and Monterey Accelerated Research System (MARS) observatories, culminating in a demonstration of a multi-observatory use case scenario. While our current efforts are in collaboration with the ocean research community, we look for opportunities to pilot test capabilities in other observatory domains.