Challenges and Opportunities for Underwater Robotics Dr. Yi Guo - PowerPoint PPT Presentation
Challenges and Opportunities for Underwater Robotics Dr. Yi Guo Department of Electrical and Computer Engineering Stevens Institute of Technology Email: yi.guo@stevens.edu In Collaboration with Dr. Brian Bingham at University of Hawaii NSF
Challenges and Opportunities for Underwater Robotics Dr. Yi Guo Department of Electrical and Computer Engineering Stevens Institute of Technology Email: yi.guo@stevens.edu In Collaboration with Dr. Brian Bingham at University of Hawaii NSF Sponsored Workshop at AAAI Conference, Austin, Jan. 25, 2015
Challenge • Recent deepwater horizon oil spill has posed great challenges to both robotics and ocean engineering communities • It is challenging to estimate the extent of the underwater plume Oil slick seen from NASA satellite. Source: Wikipedia • The challenges will continue to grow as energy production continues to happen in ever deeper water
Opportunity • Utilizing advanced robotics techniques to improve the capability of ocean robots in conducting autonomous cooperating tasks • Filling the gap between new algorithmic approaches and Contour plot of the selected ion monitoring detection from a mass spectrometer field deployments that impact coupled with acoustic positioning. Pic. from Dr. B. Bingham at Univ. of Hawaii our ability to observe, explore and manage ocean resources
Overall Objectives • The development of advanced multi- robot cooperative deployment algorithms for oceanographical applications; • The development of authentic dynamic models of operational ocean robots to integrate in advanced cooperative control algorithms; • The implementation and integration of advanced algorithms on heterogeneous ocean robots, and experimental demonstration of the efficacy in real- world coastal environments
Algorithm Development • Distributed dynamic plume tracking – Advection-diffusion equation to model oil plume distribution – Deploy multi-robots to dynamically track the plume front – Nearest neighbor communication • Challenges: – Incorporating plume dynamic model – Computational efficiency – Limited communication for multiple robots
Matlab Simulation
Towards Field Tests Algorithm development Simulation validation Controller laboratory test Simulation in Robot Simulator Field tests • Challenges: – Environment and plume data – Transition between simulation and field tests – Sensing – Vehicle dynamics – Field deployment
The Plume Model
Simulation in Field Robotics Lab Vehicle Software (FVS) Platform
Laboratory Experiment vs Field Test
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