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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