Genetic Algorithms in path planning Mansoureh Ziaei Intelligent - - PowerPoint PPT Presentation

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Genetic Algorithms in path planning Mansoureh Ziaei Intelligent Robotics Seminar, Group TAMS, University of Hamburg Agenda I. Concepts of Genetic Algorithms A. Parameters B. Limitations C. Variants D. Problem domains II. Path

SLIDE 1

Genetic Algorithms

in path planning

Mansoureh Ziaei Intelligent Robotics Seminar, Group TAMS, University of Hamburg

SLIDE 2

Agenda

I. Concepts of Genetic Algorithms A. Parameters B. Limitations C. Variants D. Problem domains II. Path planning

2 [http://imgkid.com]

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Darwin’s theory of evolution

Organisms change over time As a result of changes in heritable physical or behavioral traits To better adapt to its environment To survive and have more offspring Microevolution Macroevolution

[http://www.livescience.com] 3

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Why evolution as an inspiration for computational problems?

Searching through a huge number of possibilities Effective use of parallelism Adaptive computer programs Innovative computer programs Complex solutions

SLIDE 5

Genetic Algorithms

[http://evolutionarysystemsbiology.org]

Invented by John Holland in 1960s Goal: importing natural adaption into computer systems rather than solving specific problems

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How?

Genetic representation of the

solution domain

Fitness function to evaluate the

solution domain

Selection mechanisms
Crossover
Mutation

SLIDE 7

GAs parameters

7 [http://www.edc.ncl.ac.uk]

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Limitations

Fitness function evaluation
Scaling with complexity
Stop criterion
Local optima
Dynamic data sets
Right/wrong measure
Speed of convergence

SLIDE 9

Variants

Chromosome representation
Elitism
Parallel implementations
Adaptive GAs

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Problem domains

Timetabling and scheduling
Engineering
Global optimization problem
Pattern /speech recognition
Training neural networks
Path planning
...

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Path planning

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Autonomous robot navigation

Explore an environment independent of human presence or intervention
Uncertain environment
Generate a feasible path and optimize it in world space
Global planning vs. local planning

[2]

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Using genetic algorithms

Value-encoded scheme
Path fitness based on path length

and path feasibility

13 [2] [2]

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Applied elements

Fitness function: Rank selection Single cross point Mutation Elitism

14 [4] [4]

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Thanks! Questions?

Bibliography:

Melanie, Mitchell. "An introduction to genetic algorithms." Cambridge, Massachusetts London, England, Fifth printing 3 (1999). 2. Manikas, W., Kaveh Ashenayi, and RogerL Wainwright. "Genetic algorithms for autonomous robot navigation." Instrumentation & Measurement Magazine, IEEE 10.6 (2007): 26-31. 3. Alvarez, Alberto, Andrea Caiti, and Reiner Onken. "Evolutionary path planning for autonomous underwater vehicles in a variable ocean." Oceanic Engineering, IEEE Journal of 29.2 (2004): 418-429.

Sedighi, Kamran H., et al. "Autonomous local path planning for a mobile robot using a genetic algorithm." Evolutionary Computation, 2004. CEC2004. Congress on. Vol. 2. IEEE, 2004.