SubjectsSubjects(version: 901)
Course, academic year 2021/2022
  
Evolutionary Robotics - NAIL065
Title: Evoluční robotika
Guaranteed by: Department of Software and Computer Science Education (32-KSVI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: summer
E-Credits: 4
Hours per week, examination: summer s.:2/1 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: RNDr. František Mráz, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Is incompatible with: NAIX065
Is interchangeable with: NAIX065
Annotation -
Last update: T_KSVI (05.05.2004)
Evolutionary robotics is a technique of automatic programming of autonomous robots. The lecture shows how robot can be learned to solve tasks instead of their direct programming. Algorithms simulating natural evolution (mainly genetic algorithms with neural networks) enable the robots to evolve their abilities in interaction with their environment. In the accompanying seminary, the students will work with robot simulators and robotic kits.
Course completion requirements -
Last update: RNDr. František Mráz, CSc. (17.02.2020)

This course is taught in Czech/Slovak only. The requirements to complete the course can be found on the Czech site equivalent.

Literature - Czech
Last update: RNDr. František Mráz, CSc. (05.05.2015)

S. Nolfi, D. Floreano: Evolutionary robotics: the biology, intelligence and technology of self-organizing machines, The MIT Press, Cambridge, Massachusetts, 2000

R. C. Arkin: Behavior-based robotics, The MIT Press, Cambridge, Massachusetts, 1998

D. Floreano, C. Mattiussi: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, MIT Press, 2008

Syllabus -
Last update: RNDr. František Mráz, CSc. (05.05.2015)
  • Behavior-based robotics, robot learning, artificial life. Engineering perspective, biological perspective.
  • Genetic algorithms, artificial neural networks, neural control of a robot, evolution of neural networks, genetic programming. Robot evolution - simulated and physical.
  • Evolution of simple navigation - straight motion with obstacle avoidance.
  • Reactive intelligence, sensory-motor coordination.
  • Modular control architecture, evolution of a modular architecture.
  • Learning and evolution - two forms of (biological) adaptation.
  • Competitive co-evolution, a predator-prey model.
  • Genotype, phenotype, mapping of genotype into phenotype.
  • Evolution of complex walking robots.
  • Evolutionary learning of neural networks - algorithms SANE, ESP and NEAT.
  • Robotic swarms - examples of usage, coordinated exploration, transportation and clustering, reconfigurable robots.
  • From simulation to reality - construction of physical robots based on results from a simulation.

 
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