
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | August 27, 2012 |
Latest Amendment Date: | August 27, 2012 |
Award Number: | 1218155 |
Award Instrument: | Standard Grant |
Program Manager: |
gregory chirikjian
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2012 |
End Date: | August 31, 2015 (Estimated) |
Total Intended Award Amount: | $200,501.00 |
Total Awarded Amount to Date: | $200,501.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
ONE CASTLE POINT ON HUDSON HOBOKEN NJ US 07030-5906 (201)216-8762 |
Sponsor Congressional District: |
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Primary Place of Performance: |
NJ US 07030-5991 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Robust Intelligence |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
This collaborative project addresses the need for ocean observational techniques which was highlighted by the recent Deepwater Horizon incident. The proposed project investigates heterogeneous ocean robots (including wave gliders, unmanned surface vessels, and autonomous underwater vehicles) to detect and monitor the propagation of oil plumes. Specific objectives include: 1) the development of a distributed multi-robot cooperative deployment algorithm using partial differential equation (PDE) based methods that match the oceanographic model of oil transport, 2) the development of authentic dynamic model of the new wave glider platform to incorporate in the cooperative control, and 3) assessing the potential advantages of innovative algorithms through simulations and experimental demonstration in a coastal experiment using a network of ocean robot platforms.
Broader Impacts: The proposed project will provide novel algorithmic and software support for collective sensing, and address a pressing real-world need for better sensing of underwater hydrocarbon plumes. The techniques developed in the proposal will have long-term impacts in underwater exploration such as oceanographic survey and energy production in deep water. The results may also potentially benefit other environmental monitoring tasks with underlying diffusion and advection processes, such as weather event tracking and climate prediction. The planned work will integrate research projects with education activities through robot-centric undergraduate and graduate education, robotics competition, short course and workshop development, and outreach to K-12 education. Partnering with the Stevens' Center for Innovation in Engineering and Science Education, the project will showcase the proposed research in the curriculum of the Stevens Build IT Underwater Robotics Scale-Up for STEM Learning and Workforce Development Project awarded by NSF.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Marine pollution is one of the major environment hazards as it not only causes long-term damage to the marine environment but also leads to serious economic losses in coastal areas. For example, the Deepwater Horizon oil spill happened in the Gulf of Mexico in April 2010 causes over 8,000 animals dead just six months after the spill. The urgent priority in the oil spill response management is to determine the spatial extent of the spill, and effectively track the oil plume front. This mission poses great challenges to robotics, ocean engineering, and environmental engineering communities due to the dynamic nature of plume propagation and the complexity of oceanographical processes.
We have developed distributed tracking control of dynamic ocean pollution plumes using multiple cooperating robots. Based on an oceanographical model of pollution source dispersion, which is described by a partial differential equation named advection-diffusion equation, we proposed an estimation and control framework to dynamically estimate and track the plume front using robot onboard sensors’ measurement. Different from existing work on static level curve tracking that relies purely on gradient information, the transport model of pollution source is explicitly considered using the advection-diffusion model. The robots communicate in a nearest-neighbor topology to cooperatively track and patrol along the plume propagating front. Convergence of the developed plume tracking scheme was rigorously proved using dynamic systems and control theory, and performances were demonstrated by numerical simulations.
The multi-robot plume tracking control we developed have been tested in coastal field experiments in May and August 2015 at Makai Research Pier in Oahu, Hawaii. Two unmanned surface vehicles (USVs) were built for the experiments by one of the PIs’ lab. Rhodamine dye was used to generate a buoyant plume by pumping the dye on the surface of the sea. Two USVs were equipped with rhodamine dye fluorometers onboard to detect concentration value of the dye. Distributed plume survey and cooperative plume tracking using the two USVs have been performed. Experimental data have been collected that reveal the temporal and spatial dynamics of the chemical plume in the coastal environment. Experimental data of USV plume tracking have also been collected and documented for performance evaluation.
During the course of the project, the team has also developed simulation and emulation tools based on a robotic simulator platform, Field Robotics Lab Vehicle Software. Probabilistic environment and plume models were developed in the simulator that capture both the time-averaged, idealized structure and the instantaneous, realistic structure of a dynamic plume. Application Program Interface (API) were developed to provide tools common to many driver and control applications. Visualization packages were also developed that provide 3D visualization of the robots’ positions and orientation in both the simulation and the emulation modes. The developed tools are instrumental for future robotic research, and enable validation of advanced robotic algorithms without expensive field deployments in ocean environments.
The project has provided direct research training for two postdoc, four graduate students, and three undergraduate students at the participating universities. It also enriches a few fundamental courses on cross-disciplinary subjects with new cutting edge techniques in pervasive applications in environmental monitoring. The research results have been published in journals and international conference proceedings. The PIs have made multiple conference presentations and invited talks on the research, and disseminated the research results to the robotics and marine technology communities. Collected ocean plume data are made available for the commun...
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