Unsupervised Learning of Growing Roadmap in Multi-Goal Motion Planning Problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00224911" target="_blank" >RIV/68407700:21230/14:00224911 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.cs.unm.edu/amprg/mlpc14Workshop/schedule.html" target="_blank" >http://www.cs.unm.edu/amprg/mlpc14Workshop/schedule.html</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Learning of Growing Roadmap in Multi-Goal Motion Planning Problem

Popis výsledku v původním jazyce

In this paper, we address the multi-goal motion planning problem in which it is required to determine an order of visits of a pre-specified set of goals together with the shortest trajectories connecting the goals. The considered problem is inspired by inspection planning missions, where multiple goals must be visited with a required precision. The problem combines challenges of the combinatorial traveling salesman problem with difficulties of the motion planning. The presented approach is based on unsupervised learning of the self-organizing map technique for the traveling salesman problem applied in the configuration space. This learning technique takes an advantage of acquiring information about exploring the configuration space into a topology of the map that is simultaneously exploited in determination of the multi-goal trajectory and further directions of motion planning roadmap expansion. Presented results indicate that the proposed approach is feasible and it is able to provide

Název v anglickém jazyce

Unsupervised Learning of Growing Roadmap in Multi-Goal Motion Planning Problem

Popis výsledku anglicky

In this paper, we address the multi-goal motion planning problem in which it is required to determine an order of visits of a pre-specified set of goals together with the shortest trajectories connecting the goals. The considered problem is inspired by inspection planning missions, where multiple goals must be visited with a required precision. The problem combines challenges of the combinatorial traveling salesman problem with difficulties of the motion planning. The presented approach is based on unsupervised learning of the self-organizing map technique for the traveling salesman problem applied in the configuration space. This learning technique takes an advantage of acquiring information about exploring the configuration space into a topology of the map that is simultaneously exploited in determination of the multi-goal trajectory and further directions of motion planning roadmap expansion. Presented results indicate that the proposed approach is feasible and it is able to provide

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GP13-18316P" target="_blank" >GP13-18316P: Samo-organizující se sítě v robotických úlohách plánování cesty přes více cílů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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