Print Email Facebook Twitter Evolving State Machines as Robot Controllers Title Evolving State Machines as Robot Controllers Author den Toom, Matthijs (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Langendoen, Koen (graduation committee) Verhoeven, Chris (mentor) Nasri Nasrabadi, Mitra (graduation committee) Degree granting institution Delft University of Technology Project Zebro project Date 2019-08-23 Abstract Automated generation of robot controllers using an Evolutionary Algorithm(EA) has received increasing attention in the last years as it has the potentialfor a reduction in the development time of a robot. Often these EAs generateNeural Networks (NNs) as robot controllers. Using a NN for automaticallygenerating robot controllers has two important downsides: 1.) A human isnot able to fully understand the inner working of a multi-layer NN, and 2.)a NN has only limited abilities to decompose a complex task into sub tasks.Both of these downsides can be addressed by using a State Machine (SM)instead of a NN as robot controller. Therefore, this thesis introduces an EAcalled Evolving State Machines As Controllers (ESMAC). ESMAC generatesSMs instead of NNs. A SM is understandable for humans because ofits modularity and allows for task decomposition by using a state for eachsub task. Furthermore, two extensions of ESMAC are proposed: adaptiveESMAC and selector ESMAC. Adaptive ESMAC aims to automatically determinesthe number of states with which the best tness for a task canbe achieved. Selector ESMAC replaces the transitions that are used in aSM to switch between states with a NN-based switching mechanism. This switching mechanism allows mutations to make more gradual changes to aSM's behaviours, which improves the performance of the EA. The performance of ESMAC is evaluated on two robotic tasks: come-and-go and phototaxis-with-obstacles. All three variants of ESMAC showequally good performance as a NN-based EA on the evaluated tasks. Thecontrollers generated with standard ESMAC and adaptive ESMAC hardlymake any state transitions and mainly use one state. However, controllers that do use multiple states appear to be more robust to changing scenarios and in noisy environments. Selector ESMAC is able to generate SMs-based controllers that have complementing states and, therefore, shows potentialfor decomposing a task into sub tasks. Subject Evolutionary AlgorithmsState machines To reference this document use: http://resolver.tudelft.nl/uuid:5961f14d-8d3e-4be6-b127-010a20f8b044 Part of collection Student theses Document type master thesis Rights © 2019 Matthijs den Toom Files PDF thesis_final.pdf 1.51 MB Close viewer /islandora/object/uuid:5961f14d-8d3e-4be6-b127-010a20f8b044/datastream/OBJ/view