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A Survey of Artificial Intelligence Applications in Water-based Autonomous Vehicles

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:

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A Survey of Artificial Intelligence Applications in Water-based Autonomous Vehicles

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  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 submarine’s 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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