An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00301255" target="_blank" >RIV/68407700:21230/16:00301255 - isvavai.cz</a>

Result on the web

<a href="https://www.hindawi.com/journals/cin/2016/2720630/" target="_blank" >https://www.hindawi.com/journals/cin/2016/2720630/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2016/2720630" target="_blank" >10.1155/2016/2720630</a>

Result language

angličtina

Original language name

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

Original language description

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the roboticMTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/GP13-18316P" target="_blank" >GP13-18316P: Self-Organizing Maps for Multi-Goal Path Planning Tasks</a><br>

Continuities

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

Publication year

2016

Confidentiality

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

Name of the periodical

Computational Intelligence and Neuroscience

ISSN

1687-5273

e-ISSN

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Volume of the periodical

2016

Issue of the periodical within the volume

2720630

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

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UT code for WoS article

000379870700001

EID of the result in the Scopus database

2-s2.0-84975316786