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 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

• The challenges will continue to

grow as energy production continues to happen in ever deeper water

Oil slick seen from NASA satellite. Source: Wikipedia

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 field deployments that impact

• ur ability to observe,

explore and manage ocean resources

Contour plot of the selected ion monitoring detection from a mass spectrometer coupled with acoustic positioning. Pic. from

• Dr. B. Bingham at Univ. of Hawaii

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

• cean 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

• Challenges:

– Environment and plume data – Transition between simulation and field tests – Sensing – Vehicle dynamics – Field deployment

Algorithm development Simulation validation Simulation in Robot Simulator Controller laboratory test Field tests

The Plume Model

Simulation in Field Robotics Lab Vehicle Software (FVS) Platform

Laboratory Experiment vs Field Test