A Survey of Artificial Intelligence Applications in Water-based

Autonomous Vehicles

Daniel D. Smith

CSC 7444

December 8, 2008

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

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.

Past and Current Vehicles

Problems with traditional control methods

• Complex - especially for underwater vehicles

• Non-adaptive

• Can be slow

Neural Networksand

Self-Organizing Maps

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.

A Neural Network Controller for Diving of a Variable Mass

Autonomous Underwater Vehicle

Mazda Moattari and Alireza Khayatian

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.

Self-tuning PID Controller

Control of Underwater Autonomous Vehicles Using

Neural Networks

Michael Santora, Joel Alberts,and Dean Edwards

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.

Submarine

Controller and Neural Network

Autonomous Underwater Vehicle Guidance by

Integrating Neural Networks and Geometric Reasoning

Gian Luca Foresti, Stefani Gentili,and Massimo Zampato

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

A Self-Organizing Map Based Navigation System for an

Underwater Robot

Kazuo Ishii, Shuhei Nishada, and Tamaki Ura

SOM with Learning• 20 x 20 node map• 5000 training data sets• On-line, map adapts to the

environment.

Genetic Algorithms

A Hierarchical Global Path Planning Approach for AUV Based on Genetic Algorithm

QiaoRong Zhang

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.

Line of Sight Guidancewith Intelligent Obstacle

Avoidance for Autonomous Underwater Vehicles

Xiaoping Wu, Zhengping Feng, Jimao Zhu,and Robert Allen

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

Results of Simulation

Before Tuning After Tuning

Evolutionary Path Planning for Autonomous Underwater

Vehicles in a Variable Ocean

Alberto Alvarez, Andrea Caiti, and Reiner Onken

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.

Evolutionary Path Planning and Navigation of

Autonomous Underwater Vehicles

V. Kanakakis and N. Tsourveloudis

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:

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f = aii=1

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Questions?

Thank you