A survey of asymptotically optimal sampling-based motion planning methods
Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot’s limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guaran...
| Main Authors: | , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Annuals Reviews
2021
|
| _version_ | 1797054285015416832 |
|---|---|
| author | Gammell, JD Strub, MP |
| author_facet | Gammell, JD Strub, MP |
| author_sort | Gammell, JD |
| collection | OXFORD |
| description | Motion planning is a fundamental problem in autonomous robotics. It
requires finding a path to a specified goal that avoids obstacles and
obeys a robot’s limitations and constraints. It is often desirable for this
path to also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are
an active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge towards
the optimal solution as computational effort approaches infinity. This
survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant
ongoing research on this topic. |
| first_indexed | 2024-03-06T18:55:04Z |
| format | Journal article |
| id | oxford-uuid:118fc729-410f-47ee-b66d-6afe9b266ef4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:55:04Z |
| publishDate | 2021 |
| publisher | Annuals Reviews |
| record_format | dspace |
| spelling | oxford-uuid:118fc729-410f-47ee-b66d-6afe9b266ef42022-03-26T10:02:59ZA survey of asymptotically optimal sampling-based motion planning methodsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:118fc729-410f-47ee-b66d-6afe9b266ef4EnglishSymplectic ElementsAnnuals Reviews2021Gammell, JDStrub, MPMotion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot’s limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic. |
| spellingShingle | Gammell, JD Strub, MP A survey of asymptotically optimal sampling-based motion planning methods |
| title | A survey of asymptotically optimal sampling-based motion planning methods |
| title_full | A survey of asymptotically optimal sampling-based motion planning methods |
| title_fullStr | A survey of asymptotically optimal sampling-based motion planning methods |
| title_full_unstemmed | A survey of asymptotically optimal sampling-based motion planning methods |
| title_short | A survey of asymptotically optimal sampling-based motion planning methods |
| title_sort | survey of asymptotically optimal sampling based motion planning methods |
| work_keys_str_mv | AT gammelljd asurveyofasymptoticallyoptimalsamplingbasedmotionplanningmethods AT strubmp asurveyofasymptoticallyoptimalsamplingbasedmotionplanningmethods AT gammelljd surveyofasymptoticallyoptimalsamplingbasedmotionplanningmethods AT strubmp surveyofasymptoticallyoptimalsamplingbasedmotionplanningmethods |