6533b870fe1ef96bd12cf28f

RESEARCH PRODUCT

Resilient hexapod robot

Bakir LacevicDarko TrivunHaris Dindo

subject

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRobot kinematicseducation.field_of_studyHexapodControl and OptimizationEvent (computing)PopulationControl engineeringMicrocontrollerGait (human)machine learningComputer Networks and CommunicationGenetic algorithmArtificial IntelligenceGenetic algorithmRoboteducationresilienceInformation Systems

description

In this paper, we present a method of learning desired behaviour of the specific robotic system and transfer of the existing knowledge in the event of partial system failure. Six-legged robot (hexapod) built on top of the Bioloid platform is used for the method verification. We use genetic algorithms to optimize the hexapod's gait, after which we simulate physical damage caused to the robot. The goal of this method is to optimize the gait in accordance with the actual robot morphology, instead of the assumed one. Also, knowledge that was previously gained will be transferred in order to improve the results. Nonstandard genetic algorithm with the specific mixed population is used for this.

yearjournalcountryeditionlanguage
2017-10-01
10.1109/icat.2017.8171613http://hdl.handle.net/10447/310045