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

Result code in 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>
Result on the web
<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
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Result language
angličtina
Original language name
Unsupervised Learning of Growing Roadmap in Multi-Goal Motion Planning Problem
Original language description
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
Czech name
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Czech description
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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
S - Specificky vyzkum na vysokych skolach

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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