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A Survey of Artificial Intelligence Applications in Water-based
Autonomous Vehicles
Daniel D. Smith
CSC 7444
December 8, 2008
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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
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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.
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Past and Current Vehicles
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Problems with traditional control methods
• Complex - especially for underwater vehicles
• Non-adaptive
• Can be slow
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Neural Networksand
Self-Organizing Maps
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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.
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A Neural Network Controller for Diving of a Variable Mass
Autonomous Underwater Vehicle
Mazda Moattari and Alireza Khayatian
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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.
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Self-tuning PID Controller
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Control of Underwater Autonomous Vehicles Using
Neural Networks
Michael Santora, Joel Alberts,and Dean Edwards
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Submarine Guidance
• Simulation for control of a submarine’s heading and depth
• Assumptions:– No obstacles– Constant speed– Waypoint reached if location was within a
1m radius circle of the actual waypoint.
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Submarine
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Controller and Neural Network
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Autonomous Underwater Vehicle Guidance by
Integrating Neural Networks and Geometric Reasoning
Gian Luca Foresti, Stefani Gentili,and Massimo Zampato
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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
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A Self-Organizing Map Based Navigation System for an
Underwater Robot
Kazuo Ishii, Shuhei Nishada, and Tamaki Ura
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SOM with Learning• 20 x 20 node map• 5000 training data sets• On-line, map adapts to the
environment.
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Genetic Algorithms
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A Hierarchical Global Path Planning Approach for AUV Based on Genetic Algorithm
QiaoRong Zhang
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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.
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Line of Sight Guidancewith Intelligent Obstacle
Avoidance for Autonomous Underwater Vehicles
Xiaoping Wu, Zhengping Feng, Jimao Zhu,and Robert Allen
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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
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Results of Simulation
Before Tuning After Tuning
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Evolutionary Path Planning for Autonomous Underwater
Vehicles in a Variable Ocean
Alberto Alvarez, Andrea Caiti, and Reiner Onken
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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.
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Evolutionary Path Planning and Navigation of
Autonomous Underwater Vehicles
V. Kanakakis and N. Tsourveloudis
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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:
€
f = aii=1
6
∑ ∗ f i
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Questions?
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Thank you