Adaptive Behaviour Based Robotics using On-Board Genetic Programming
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This thesis investigates the use of Genetic Programming (GP) to evolve controllers for an autonomous robot. GP is a type of Genetic Algorithm (GA) using the Darwinian idea of natural selection and genetic recombination, where the individuals most often is represented as a tree-structure. The GP is used to evolve a population of possible solutions over many generations to solve problems. The most common approach used today, to develop controllers for autonomous robots, is to employ a GA to evolve an Artificial Neural Network (ANN). This approach is most often used in simulation only or in conjunction with online evolution; where simulation still covers the largest part of the process. The GP has been largely neglected in Behaviour Based Robotics (BBR). The is primarily due to the problem of speed, which is the biggest curse of any standard GP. The main contribution of this thesis is the approach of using a linear representation of the GP in online evolution, and to establish whether or not the GP is feasible in this situation. Since this is not a comparison with other methods, only a demonstration of the possibilities with GP, there is no need for testing the particular test cases with other methods. The work in this thesis builds upon the work by Wolfgang Banzhaf and Peter Nordin, and therefore a comparison with their work will be done.