dc.creator | Faria, Margarida | es |
dc.creator | Marín, Ricardo | es |
dc.creator | Popovi´c, Marija | es |
dc.creator | Maza Alcañiz, Iván | es |
dc.creator | Viguria, Antidio | es |
dc.date.accessioned | 2019-04-16T11:01:09Z | |
dc.date.available | 2019-04-16T11:01:09Z | |
dc.date.issued | 2019-01 | |
dc.identifier.citation | Faria, M., Marín, R., Popovi´c, M., Maza Alcañiz, I. y Viguria, A. (2019). Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV. Sensors, 19 (1) | |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/85719 | |
dc.description.abstract | Exploring large, unknown, and unstructured environments is challenging for Unmanned
Aerial Vehicles (UAVs), but they are valuable tools to inspect large structures safely and efficiently.
The Lazy Theta* path-planning algorithm is revisited and adapted to generate paths fast enough
to be used in real time and outdoors in large 3D scenarios. In real unknown scenarios, a given
minimum safety distance to the nearest obstacle or unknown space should be observed, increasing
the associated obstacle detection queries, and creating a bottleneck in the path-planning algorithm.
We have reduced the dimension of the problem by considering geometrical properties to speed up
these computations. On the other hand, we have also applied a non-regular grid representation
of the world to increase the performance of the path-planning algorithm. In particular, a sparse
resolution grid in the form of an octree is used, organizing the measurements spatially, merging
voxels when they are of the same state. Additionally, the number of neighbors is trimmed to match
the sparse tree to reduce the number of obstacle detection queries. The development methodology
adopted was Test-Driven Development (TDD) and the outcome was evaluated in real outdoors
flights with a multirotor UAV. In the results, the performance shows over 90 percent decrease in
overall path generation computation time. Furthermore, our approach scales well with the safety
distance increases. | es |
dc.description.sponsorship | Unión Europea, Horizonte 2020 Marie Sklodowska-Curie Nº 64215 | es |
dc.description.sponsorship | Unión Europea. MULTIDRONE H2020-ICT-731667 | es |
dc.description.sponsorship | Unión Europea. AEROARMS H2020-ICT-644271 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 19 (1) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Path planning | es |
dc.subject | UAV | es |
dc.subject | Autonomous exploration | es |
dc.subject | Sparse grids | es |
dc.subject | Lazy Theta | es |
dc.title | Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | Marie Sklodowska-Curie Nº 64215 | es |
dc.relation.projectID | H2020-ICT-731667 | es |
dc.relation.projectID | H2020-ICT-644271 | es |
dc.relation.publisherversion | https://doi.org/10.3390/s19010174 | es |
dc.identifier.doi | 10.3390/s19010174 | es |
dc.contributor.group | Universidad de Sevilla. TEP151: Robótica, Visión y Control | es |
idus.format.extent | 21 p. | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 19 | es |
dc.publication.issue | 1 | es |