Evolution of multi-loop controllers for fixed morphology with a cyclic genetic algorithm

ABSTRACT

Cyclic genetic algorithms can be used to generate single loop control programs for robots. While successful in generating controllers for individual leg movement, gait generation, and area search path finding, cyclic genetic algorithms have had limited use when dealing with control problems that require different behaviors in response to sensor inputs. For such behaviors, there is a need for modifications that will allow the generation of multi-loop control programs, which can properly react to sensor input. In this work, we present modifications to the standard cyclic genetic algorithm that enables it to learn multi-loop control programs with branching that allows the control to jump from one loop to another. Preliminary tests show the success of our modification.

REFERENCES

• 1: Parker, G., and Rawlins, G. Cyclic Genetic Algorithms for the Locomotion of Hexapod Robots. Proceedings of the World Automation Congress, Volume 3, Robotic and Manufacturing Systems, 1996.
• 2: Robotics Invention Systems 2.0 Constructopedia. LEGO Mindstorms, 2000.
• 3: Dave Baum, Definitive Guide to LEGO MINDSTORMS, APress L. P., 2002

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.9 Robotics
          Subjects: Autonomous vehicles

General Terms:
Algorithms, Experimentation

Keywords:
code generation, control, evolutionary robotics, learning