Online planning for multi-robot active perception with self-organising maps
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00316691" target="_blank" >RIV/68407700:21230/18:00316691 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10514-017-9691-4" target="_blank" >https://link.springer.com/article/10.1007/s10514-017-9691-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10514-017-9691-4" target="_blank" >10.1007/s10514-017-9691-4</a>
Alternative languages
Result language
angličtina
Original language name
Online planning for multi-robot active perception with self-organising maps
Original language description
We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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/GJ15-09600Y" target="_blank" >GJ15-09600Y: Adaptive Informative Path Planning in Autonomous Data Collection in Dynamic Unstructured Environments</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
Name of the periodical
Autonomous Robots
ISSN
0929-5593
e-ISSN
1573-7527
Volume of the periodical
2018
Issue of the periodical within the volume
42
Country of publishing house
US - UNITED STATES
Number of pages
24
Pages from-to
715-738
UT code for WoS article
000427378300003
EID of the result in the Scopus database
2-s2.0-85038028527