Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Planning in high-dimensional spaces"'
Autor:
Petrović, Luka
Motion planning is a key tool that allows robots to navigate through an environment without collisions. The problem of robot motion planning has been studied in great detail over the last several decades, with researchers initially focusing on system
Externí odkaz:
http://arxiv.org/abs/1806.07457
Publikováno v:
IEEE Access, Vol 8, Pp 42619-42632 (2020)
Increasing the dimensionality of the configuration space quickly makes trajectory planning computationally intractable. This paper presents an efficient motion planning approach that exploits the heterogeneous low-dimensional structures of a given pl
Externí odkaz:
https://doaj.org/article/639974f34f3c49599298cbeb907852e9
Akademický článek
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Akademický článek
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Autor:
Petrovi��, Luka
Motion planning is a key tool that allows robots to navigate through an environment without collisions. The problem of robot motion planning has been studied in great detail over the last several decades, with researchers initially focusing on system
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4cfa40a424976c2a5c37edda7826cc71
http://arxiv.org/abs/1806.07457
http://arxiv.org/abs/1806.07457
Autor:
Paul Vernaza, Daniel D. Lee
Publikováno v:
The International Journal of Robotics Research. 31:1739-1760
We present a class of methods for optimal holonomic planning in high-dimensional spaces that automatically learns and leverages low-dimensional structure to efficiently find high-quality solutions. These methods are founded on the principle that prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::591a04d2eb66f1d4ec194374e573a28a
https://doi.org/10.1177/0278364912457436
https://doi.org/10.1177/0278364912457436
Publikováno v:
ICRA
We present hierarchical rejection sampling (HRS) to improve the efficiency of asymptotically optimal sampling-based planners for high-dimensional problems with differential constraints. Pruning nodes and rejecting samples that cannot improve the curr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a90ec010eb156da1611308e0abb2e993
https://doi.org/10.1109/icra.2016.7487120
https://doi.org/10.1109/icra.2016.7487120
Autor:
Paul Vernaza, Daniel Lee
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 25:1126-1132
We present a novel learning-based method for generating optimal motion plans for high-dimensional motion planning problems. In order to cope with the curse of dimensional- ity, our method proceeds in a fashion similar to block co- ordinate descent in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd02f1e8cc7f1bfa1e14ff893e7614b2
https://doi.org/10.1609/aaai.v25i1.7993
https://doi.org/10.1609/aaai.v25i1.7993
Conference
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Publikováno v:
ICRA
This paper presents a new motion planner, Search Tree with Resolution Independent Density Estimation (STRIDE), designed for rapid exploration and path planning in high-dimensional systems (greater than 10). A Geometric Near-neighbor Access Tree (GNAT
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f73b56a3fdd2a753bec94bd11fdf01d
https://doi.org/10.1109/icra.2013.6630908
https://doi.org/10.1109/icra.2013.6630908