Real-Time Localization of a Magnetic Anomaly : A Study of the Effectiveness of a Genetic Algorithm for Implementation on an Autonomous Underwater Vehicle

2018 ◽  
Author(s):  
Harryel Philippeaux ◽  
Manhar Dhanak
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Magnetic Anomaly ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle

3D real-time tracking, following and imaging of white sharks with an Autonomous Underwater Vehicle

2015 ◽  
Author(s):  
Amy L. Kukulya ◽  
Roger Stokey ◽  
Robin Littlefield ◽  
Frederic Jaffre ◽  
Edgar Mauricio Hoyos Padilla ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Real Time Tracking

scholarly journals An Overview of Autonomous Underwater Vehicle Research and Testbed at PeRL

2009 ◽  
Vol 43 (2) ◽  
pp. 33-47 ◽  
Author(s):  
Hunter C. Brown ◽  
Ayoung Kim ◽  
Ryan M. Eustice
Keyword(s):  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
University Of Michigan ◽  
Major Research ◽  
Research Areas ◽  
Localization And Mapping ◽  
Navigation And Control ◽  
The University ◽  
And Control

AbstractThis article provides a general overview of the autonomous underwater vehicle (AUV) research thrusts being pursued within the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Founded in 2007, PeRL's research centers on improving AUV autonomy via algorithmic advancements in environmentally based perceptual feedback for real-time mapping, navigation, and control. Our three major research areas are (1) real-time visual simultaneous localization and mapping (SLAM), (2) cooperative multi-vehicle navigation, and (3) perception-driven control. Pursuant to these research objectives, PeRL has developed a new multi-AUV SLAM testbed based upon a modified Ocean-Server Iver2 AUV platform. PeRL upgraded the vehicles with additional navigation and perceptual sensors for underwater SLAM research. In this article, we detail our testbed development, provide an overview of our major research thrusts, and put into context how our modified AUV testbed enables experimental real-world validation of these algorithms.

scholarly journals Using Genetic Algorithms to Optimize Bathymetric Sampling for Predictive Model Input

2012 ◽  
Vol 29 (3) ◽  
pp. 464-477 ◽  
Author(s):  
Dinesh Manian ◽  
James M. Kaihatu ◽  
Emily M. Zechman
Keyword(s):  
Genetic Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Forecast Model ◽  
Optimization Method ◽  
Underwater Vehicle ◽  
Bathymetric Data ◽  
Test Study ◽  
Limited Length ◽  
The Given ◽  
Sampling Pattern

Abstract This paper describes the use of an optimization method to effectively reduce the required bathymetric sampling for forcing a numerical forecast model by using the model’s sensitivity to this input. A genetic algorithm is developed to gradually evolve the survey path for a ship, autonomous underwater vehicle (AUV), or other measurement platform to an optimum, with the resulting effect of the corresponding measured bathymetry on the model used as a metric. Starting from an initial simulated set of possible random or heuristic sampling paths over the given bathymetry using certain constraints like limited length of track, the algorithm can be used to arrive at the path that would provide the best possible input to the model under those constraints. This suitability is tested by a comparison of the model results obtained by using these new simulated observations, with the results obtained using the most recent and complete bathymetric data available. Two test study areas were considered, and the algorithm was found to consistently converge to a sampling pattern that best captured the bathymetric variability critical to the model prediction.

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