Bounded Sub-optimal Multi-Robot Path Planning Using Satisfiability Modulo Theory (SMT) Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00348042" target="_blank" >RIV/68407700:21240/20:00348042 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IROS45743.2020.9341047" target="_blank" >https://doi.org/10.1109/IROS45743.2020.9341047</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS45743.2020.9341047" target="_blank" >10.1109/IROS45743.2020.9341047</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bounded Sub-optimal Multi-Robot Path Planning Using Satisfiability Modulo Theory (SMT) Approach

Popis výsledku v původním jazyce

Multi-robot path planning (MRPP) is a task of planning collision free paths for a group of robots in a graph. Each robot starts in its individual starting vertex and its task is to reach a given goal vertex. Existing techniques for solving MRPP optimally under various objectives include search-based and compilation-based approaches. Often however finding an optimal solution is too difficult hence sub-optimal algorithms that trade-off the quality of solutions and the runtime have been devised. We suggest eSMT-CBS, a new bounded suboptimal algorithm built on top of recent compilation-based method for optimal MRPP based on satisfiability modulo theories (SMT). We compare eSMT-CBS with ECBS, a major representative of bounded sub-optimal search-based algorithms. The experimental evaluation shows significant advantage of eSMT-CBS across variety of scenarios.

Název v anglickém jazyce

Bounded Sub-optimal Multi-Robot Path Planning Using Satisfiability Modulo Theory (SMT) Approach

Popis výsledku anglicky

Multi-robot path planning (MRPP) is a task of planning collision free paths for a group of robots in a graph. Each robot starts in its individual starting vertex and its task is to reach a given goal vertex. Existing techniques for solving MRPP optimally under various objectives include search-based and compilation-based approaches. Often however finding an optimal solution is too difficult hence sub-optimal algorithms that trade-off the quality of solutions and the runtime have been devised. We suggest eSMT-CBS, a new bounded suboptimal algorithm built on top of recent compilation-based method for optimal MRPP based on satisfiability modulo theories (SMT). We compare eSMT-CBS with ECBS, a major representative of bounded sub-optimal search-based algorithms. The experimental evaluation shows significant advantage of eSMT-CBS across variety of scenarios.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-17966S" target="_blank" >GA19-17966S: intALG-MAPFg: Inteligentní algoritmy pro zobecněné varianty multi-agetního hledání cest</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems

ISBN

978-1-7281-6212-6

ISSN

2153-0858

e-ISSN

—

Počet stran výsledku

7

Strana od-do

11631-11637

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Las Vegas

Datum konání akce

24. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—