Advanced Evolutionary - Embedded Intelligent Robotics - Lecture Slides, Slides of Robotics

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Advanced

Examples and

Ideas

Three Layer Evolutionary Approach

Evolve Behaviors Evolve Motions Evolve Perceptions Global perceptions, possibly encoded such as “narrow Corridor” or “beautiful Princess” Local perceptions, such as “bald head” or “long beard” Behaviors such as “go forward until you find a wall, else turn randomly right or left Encoded behaviors or internal states Time intervals Motions as timed sequences of encoded actions, for instance RFRFLL docsity.com

Evolve in hierarchy

avoid

obstacles

Execute

optimal

motions

Save energy

Look for energy

sources in

advance

Execute actions

that you enjoy

What if robot likes to play

soccer and sees the ball

but is low on energy?

Optimizing a motion

Parking a

Truck

Question; How to represent the chromosomes? Here you see several snapshots of a “movie” about parking a truck, stages of the solution process. docsity.com

• t is time u

Similar to Braitenberg Vehicle but has 8 sensors

how

• How would you

represent

chromosomes?

• Design

Crossovers?

Input and output data are some form of MV logic

1. Robot can move freely but has to avoid obstacles
2. This can be like the lowest level of behaviors in subsumption or other behavioral architecture for all your robots

Number of collisions

• Time of

learning

When you train longer you decrease the number of collisions

Applications and Problems

Evolutionary Methods

• Optimization problems:
  • Single objective optimization problems
  • Multi-Objective optimization Problems

• Search Problems (Path search)
• Optimal multi-robot coordination
• Multi-task optimization
• Optimal motion planning of robot arms (Trajectory planning of manipulators )
• Motion optimization (optimization of controller parameters - morphology in different control schemas) - PID (PI) - Fuzzy - Neural - Hybrid (neuro-fuzzy)
• Path planning and tracking (mobile robots)
• Optimal motion planning of robot arms
  • Trajectory planning of manipulators
• Vision – computational optimization

More examples of problems in which we use

evolutionary algorithms and similar methods.

GA-operators

• Selection
  • Roulette
  • Tournament
  • Stochastic sampling
  • Rank based selection
  • Boltzmann selection
  • Nonlinnear ranking selection
• Crossover
  • One point
  • Multiple points
• Mutation

Read in Auxiliary Slides about

these methods.

Or invent your own operators

for your problem.

Your design parameters to be

decided

• Genotype length
  • Fixed length genotype
  • Variable-length genotype
• Population
  • Fixed population
  • Variable population
  • Species inside population
  • Geometrical separation