Upravljanje gibanjem manipulatora u prostoru s preprekama primjenom dubokog potpornog učenja

master's thesis

• Title (english): Motion control of a manipulator in environment with obstacles using deep reinforcement learning
• Author: Marija Ančić
• Mentor: Marija Seder (mentor)
• Committee member: Marija Seder (predsjednik povjerenstva)
• Committee member: Ivan Marković (član povjerenstva)
• Committee member: Ivan Petrović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U ovom diplomskom radu mjeri se uspješnost primjene algoritama dubokog potpornog učenja za pronalazak rješenja problema Inverzne kinematike zglobova manipulatora. Eksperimentalni dio se provodi na robotu Baxteru koji ima sedam stupnjeva slobode. Inverzna kinematika je problem koji se sastoji od pronalaženja orijentacije i pozicije pojedinog zgloba kako bi manipulator došao u željenu točku. Analitičko rješenje ovog problema ne postoji stoga su se algoritmi dubokog potpornog učenja pokazali kao dobra metoda za pronalazak adekvatnog rješenja. U radu su opisane opće karakteristike robota, metoda učenja, primijenjeni algoritam i na kraju dobiveni eksperimentalni rezultati.

Abstract (english)

This thesis measures the success of using deep reinforcement learning algorithms for finding solutions to the Inverse kinematics problem. The experimental part is performed on robot Baxter that has seven degrees of freedom. Inverse kinematics is a problem that consists of finding the orientation and position of an individual joint in order for the manipulator to reach the desired point.There is no analytical solution to this problem, so deep reinforcement learning algorithms have proven to be a good method for finding an adequate solution. The paper describes the general characteristics of robots, learning methods, applied algorithm and finally obtained experimental results.

• Keywords: robot Baxter; inverzna kinematika; duboko potporno učenje; PPO; robotika
• Keywords (english): robot Baxter; inverse kinematics; deep reinforcement learning; PPO; robotics
• Language: croatian
• URN:NBN: urn:nbn:hr:168:252735
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 09:41:07