magistrsko delo magistrskega študijskega programa II. stopnje Strojništvo
Martin Knap (Author), Rok Vrabič (Mentor)

Abstract

Moderne rešitve na področju logistike obsegajo uporabo sistemov z več mobilnimi roboti. Pogoj za uspešno implementacijo takšnega sistema je učinkovit sistem dodeljevanja transportnih naročil. Ta mora skupini robotov transportna naročila dodeljevati tako, da jih ta opravijo čim več. Problem smo prepoznali kot problem optimizacije več kriterijev. Cilj naloge je razvoj algoritma, ki problem dodeljevanja rešuje na osnovi vzpodbujevalnega učenja. Algoritem smo razvijali v programskem okolju ROS. Razvili smo dodeljevalni algoritem s poudarkom na hitremu učenju. Razviti algoritem smo preizkusili v simuliranemu preizkusu. Njegovo delovanje smo primerjali z algoritmi, ki naročila dodeljujejo na osnovi preprostih pravil in izpolnjujejo le en kriterij problema. Vsak način dodeljevanja smo preizkušali v preizkusu, ki je trajal eno uro. Pri dodeljevanju z razvitim algoritmom so roboti zaključili največje število transportnih naročil. Meritve prepotovanih razdalj in časov opravljanja nalog so potrdile enostranskost preprostih pravil ter večkriterijski proces odločanja razvitega algoritma.

Keywords

magistrske naloge;robotika;optimizacija;vzpodbujevalno učenje;dodeljevanje naročil;sistem mobilnih robotov;ROS platforma;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FS - Faculty of Mechanical Engineering
Publisher: [M. Knap]
UDC: 007.52:519.8:004.8(043.2)
COBISS: 38621699 Link will open in a new window
Views: 358
Downloads: 74
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Other data

Secondary language: English
Secondary title: Reinforcement learning-based task assignment in a mobile robot system
Secondary abstract: Modern logistic solutions encompass the use of mobile robot systems. To achieve a successful implementation of such a system, one must consider an efficient design of transportation task assignment system. Main responsibility of a task assignment system is to allocate tasks in such manner that as many tasks get completed in a given time frame. This problem is recognized as a multicriteria optimization problem. The purpose of this thesis is to develop a task assignment algorithm that is based on reinforcement learning. The proposed algorithm was developed using ROS platform. We developed an algorithm with an emphasis on fast learning. The proposed algorithm was tested in a simulated environment. It was tested alongside simple task assignment rules that meet only single criterion of an assignment problem. Every task assignment algorithm was tested in an hour long experiment. Robots managed to complete the highest number of tasks in the case of the developed solution. Measurements of traveled distances and task completion times confirmed the one-sided decisions of simple rules, and the multicriteria decision-making process of the developed algorithm.
Secondary keywords: master thesis;robotics;optimization;reinforcement learning;task assignment;mobile robot system;ROS platform;
Type (COBISS): Master's thesis/paper
Study programme: 0
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. Ljubljana, Fak. za strojništvo
Pages: XXIV, 75 str.
ID: 12114464
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, magistrsko delo magistrskega študijskega programa II. stopnje Strojništvo