On Unsupervised Learning based Multi-Goal Path Planning for Visiting 3D Regions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00328622" target="_blank" >RIV/68407700:21230/18:00328622 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/citation.cfm?id=3297099" target="_blank" >https://dl.acm.org/citation.cfm?id=3297099</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3297097.3297099" target="_blank" >10.1145/3297097.3297099</a>
Alternative languages
Result language
angličtina
Original language name
On Unsupervised Learning based Multi-Goal Path Planning for Visiting 3D Regions
Original language description
In this paper, we report on our early results on deploying unsupervised learning technique for solving a multi-goal path planning problem to determine a shortest path to visit a given set of 3D regions. The addressed problem is motivated by data collection missions in which a robotic vehicle is requested to visit a set of locations to perform particular measurements. Instead of precise visitation of the specified locations, it is allowed to take the measurements at the respective distance from the locations, and thus save the travel cost by exploiting non-zero sensing radius of the vehicle. In particular, the problem is formulated as a 3D variant of the Close-Enough Traveling Salesman Problem (CETSP), and the proposed approach is based on the recently introduced technique called the Growing Self-Organizing Array (GSOA). The GSOA is a neural network for routing problems that is accompanied with unsupervised learning procedure to determine a solution of the TSP-like problems in a finite number of learning epochs. Based on the reported results, the proposed GSOA-based approach provides competitive or better results than existing combinatorial heuristics based on the so-called Steiner zones, while the computational requirements are significantly lower.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA16-24206S" target="_blank" >GA16-24206S: Efficient Information Gathering with Dubins Vehicles in Persistent Monitoring and Surveillance Missions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2018 International Conference on Robotics and Artificial Intelligence
ISBN
978-1-4503-6584-0
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
45-50
Publisher name
ACM
Place of publication
New York
Event location
Guangzhou
Event date
Nov 17, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—