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A Survey of Artificial Intelligence Applications in Waterbased Autonomous Vehicles

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Vehicle which can perform all the functions required of it without outside ... Pollution tracking ... System developed to compensate for changing dynamics of vehicle ... – PowerPoint PPT presentation

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Title: A Survey of Artificial Intelligence Applications in Waterbased Autonomous Vehicles


1
A Survey of Artificial Intelligence Applications
in Water-based Autonomous Vehicles
  • Daniel D. Smith
  • CSC 7444
  • December 8, 2008

2
Autonomous Vehicles
  • Vehicle which can perform all the functions
    required of it without outside intervention while
    operating in an uncontrolled environment.
  • Types
  • Land-based
  • Water-based (surface and underwater)
  • Air-based

3
Past and Current Research in Biological
Engineering
  • Program uses Autonomous Water-based vehicles for
    a variety of purposes
  • Water quality monitoring
  • Bird predation reduction
  • Pollution tracking
  • Research is moving into areas involving multiple
    agents which need to interact with each other and
    the environment in intelligent ways.

4
Past and Current Vehicles
5
Problems with traditional control methods
  • Complex - especially for underwater vehicles
  • Non-adaptive
  • Can be slow

6
Neural NetworksandSelf-Organizing Maps
7
Neural Networks
  • Some systems use the neural network along side a
    more traditional controller to provide on-line
    adjustments to the controller itself.
  • Other systems utilize the neural network as one
    stage of a multi-stage process.

8
A Neural Network Controller for Diving of a
Variable Mass Autonomous Underwater Vehicle
Mazda Moattari and Alireza Khayatian
9
Variable Mass Submarine
  • System developed to compensate for changing
    dynamics of vehicle
  • As vehicle burns fuel, the mass of the vehicle
    changes
  • Neural network provides correction to traditional
    PID control system to keep dive angle correct.
  • Correction is done by using a second neural
    network to estimate the Jacobian of the output of
    the control system.

10
Self-tuning PID Controller
11
Control of Underwater Autonomous Vehicles Using
Neural Networks
Michael Santora, Joel Alberts, and Dean Edwards
12
Submarine Guidance
  • Simulation for control of a submarines heading
    and depth
  • Assumptions
  • No obstacles
  • Constant speed
  • Waypoint reached if location was within a 1m
    radius circle of the actual waypoint.

13
Submarine
14
Controller and Neural Network
15
Autonomous Underwater Vehicle Guidance by
Integrating Neural Networks and Geometric
Reasoning
Gian Luca Foresti, Stefani Gentili, and Massimo
Zampato
16
Vision-based Guidance
  • Neural network used as the first stage of a two
    stage artificial vision system
  • Neural network is trained on test images to help
    locate the edges of underwater pipelines.
  • After training, correctly classified 93 of 100
    test images.

Training Image
Classified Image
17
A Self-Organizing Map Based Navigation System for
an Underwater Robot
Kazuo Ishii, Shuhei Nishada, and Tamaki Ura
18
SOM with Learning
  • 20 x 20 node map
  • 5000 training data sets
  • On-line, map adapts to the environment.

19
Genetic Algorithms
20
A Hierarchical Global Path Planning Approach for
AUV Based on Genetic Algorithm
QiaoRong Zhang
21
GA Description
  • Use octree to decompose 3D space into uniform
    regions.
  • Label cells as Full, Empty, or Mixed
  • GA constructs path from Source to Goal through
    Empty and Mixed Cells
  • Uses 3 genetic operations
  • Reproduction Fit individuals (paths) progress to
    the next generation
  • Crossover Create new individuals from the
    fittest of the previous population
  • Mutation (Insert, Delete, Replace)
  • Fitness is a combination of shortest distance and
    most empty cells in path.

22
Line of Sight Guidancewith Intelligent Obstacle
Avoidance for Autonomous Underwater Vehicles
Xiaoping Wu, Zhengping Feng, Jimao Zhu, and
Robert Allen
23
Tuning Fuzzy Logic with GA
  • AUV has fuzzy logic planner
  • 2 inputs Distance and angle to obstacle
  • 1 output Heading correction to avoid
  • GA used to minimize cross-track error by tuning
    the fuzzy logic planner
  • Fitness is determined by smallest cross-track
    error over a safe distance
  • Percentage of fit individuals of each population
    kept for next generation

24
Results of Simulation
Before Tuning
After Tuning
25
Evolutionary Path Planning for Autonomous
Underwater Vehicles in a Variable Ocean
Alberto Alvarez, Andrea Caiti, and Reiner Onken
26
Optimizing energy cost
  • Population is N randomly generated potential
    paths from source to goal
  • Fitness is determined by computing the energy
    cost of moving the vehicle along the path taking
    into account ocean currents.
  • N/2 individuals with lowest cost (fittest) chosen
  • Parents and offspring kept
  • Mutation is limited to the less fit individuals
    of the population and involves randomly moving
    one waypoint of the path.

27
Evolutionary Path Planning and Navigation of
Autonomous Underwater Vehicles
V. Kanakakis and N. Tsourveloudis
28
B-Spline Genetic Algorithm
  • Off-line path planning
  • B-Spline path defined by
  • Start, End, and Second Point
  • Six free-to-move points
  • Population size of 30
  • Single point crossover with mutation
  • Fitness function defined by

29
Questions?
30
Thank you
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