Evolving robot sub-behaviour modules using Gene Expression Programming
Many approaches to AI in robotics use a multi-layered approach to determine levels of behaviour from basic operations to goal-directed behaviour, the most well-known of which is the subsumption architecture. In this paper, the performances of the unigenic Gene Expression Programming (ugGEP) and multigenic GEP (mgGEP) in evolving robot controllers for a wall following robot are analysed. Additionally, the paper introduces Regulatory Multigenic Gene Expression Programming, a new evolutionary technique that can be utilised to automatically evolve modularity in robot behaviour. The proposed technique extends the mgGEP algorithm, by incorporating a regulatory gene as part of the GEP chromosome. The regulatory gene, just as in systems biology, determines which of the genes in the chromosome to express and therefore how the controller solves the problem. In the initial experiments, the proposed algorithm is implemented for a robot wall following problem and the results compared to that of ugGEP and mgGEP. In addition to the wall following behaviour, a robot foraging behaviour is implemented with the aim of investigating whether the position of a specific module (sub-expression tree) in the overall expression tree is of importance when coding for a problem.
gene expression programming subsumption architecture layered learning evolutionary robotics robot behaviour coordination