Title: A Survey of Artificial Intelligence Applications in Water-based Autonomous Vehicles
1A Survey of Artificial Intelligence Applications
in Water-based Autonomous Vehicles
- Daniel D. Smith
- CSC 7444
- December 8, 2008
2Autonomous 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
3Past 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.
4Past and Current Vehicles
5Problems with traditional control methods
- Complex - especially for underwater vehicles
- Non-adaptive
- Can be slow
6Neural NetworksandSelf-Organizing Maps
7Neural 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.
8A Neural Network Controller for Diving of a
Variable Mass Autonomous Underwater Vehicle
Mazda Moattari and Alireza Khayatian
9Variable 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.
10Self-tuning PID Controller
11Control of Underwater Autonomous Vehicles Using
Neural Networks
Michael Santora, Joel Alberts, and Dean Edwards
12Submarine 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.
13Submarine
14Controller and Neural Network
15Autonomous Underwater Vehicle Guidance by
Integrating Neural Networks and Geometric
Reasoning
Gian Luca Foresti, Stefani Gentili, and Massimo
Zampato
16Vision-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
17A Self-Organizing Map Based Navigation System for
an Underwater Robot
Kazuo Ishii, Shuhei Nishada, and Tamaki Ura
18SOM with Learning
- 20 x 20 node map
- 5000 training data sets
- On-line, map adapts to the environment.
19Genetic Algorithms
20A Hierarchical Global Path Planning Approach for
AUV Based on Genetic Algorithm
QiaoRong Zhang
21GA 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.
22Line of Sight Guidancewith Intelligent Obstacle
Avoidance for Autonomous Underwater Vehicles
Xiaoping Wu, Zhengping Feng, Jimao Zhu, and
Robert Allen
23Tuning 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
24Results of Simulation
Before Tuning
After Tuning
25Evolutionary Path Planning for Autonomous
Underwater Vehicles in a Variable Ocean
Alberto Alvarez, Andrea Caiti, and Reiner Onken
26Optimizing 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.
27Evolutionary Path Planning and Navigation of
Autonomous Underwater Vehicles
V. Kanakakis and N. Tsourveloudis
28B-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
29Questions?
30Thank you