Advanced Examples and Ideas. Three Layer Evolutionary Approach. Local perceptions, such as “bald head” or “long beard”. Encoded behaviors or internal states. Time intervals. Evolve Behaviors. Evolve Motions. Evolve Perceptions.
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Local perceptions, such as “bald head” or “long beard”
Encoded behaviors or internal states
Motions as timed sequences of encoded actions, for instance RFRFLL
Global perceptions, possibly encoded such as “narrow Corridor” or “beautiful Princess”
Behaviors such as “go forward until you find a wall, else turn randomly right or left
Go to the end of the corridor and then look for food
If you see a beautiful princess go to her and bow low.
If you see a dragon escape
Execute optimal motions
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?
Parking a Truck
Solving this analytically would be very difficult
Here you see several snapshots of a “movie” about parking a truck, stages of the solution process.
Learning Obstacle Avoiding
Robot can move freely but has to avoid obstacles
This can be like the lowest level of behaviors in subsumption or other behavioral architecture for all your robots
The key to success is often in fitness function
When you train longer you decrease the number of collisions
More examples of problems in which we use evolutionary algorithms and similar methods.
What are these “other algorithm”? algorithms and similar methods.
You can try them in your homework 1 if GA or GP is too easy for you.
Using them gives you higher possibility of creating a successful superior method for a new problem
Read in Auxiliary Slides about these methods. Or invent your own operators for your problem.
Trajectory Planning Problems
Projects last years
Design of robotic controllers algorithms and similar methods.
Use of Neural Networks algorithms and similar methods.
Step 1: evaluate population;
Step 2: eliminate bad rules and fill up population;
Step 3: scale the fitness values;
Step 4: repeat NI iterations for Step 4 to Step 9
Step 5: select the individuals of the population;
Step 6: crossover and mutate the individuals;
Step 7: evaluate population;
Step 8: eliminate bad rules and fill up population;
Step 9: scale the fitness values.
Step 10: Add the best rule to the final rule set.
Step 11: Penalize the selected rule.
Step 12: If the stop conditions are not fulfilled go to Step 1
Pk is the kinematic model
Find the set of joint paths, next smooth it
Minimize the cumulative error
Fot – excessive driving (sum of all maximum torques), fq – the total joint traveling distance of the manipulator, fc - total Cartesian trajectory length, tT - total consumed time for robot motion
Drug Delivery Problem
Pareto solutions for different algorithms
Optimization of Airplane Wings
Three-dimensional the blue method Minimization Problem
Pareto Front the blue method
example the blue method