Reciprocally-Rotating Velocity Obstacles
Abstract — Modern multi-agent systems frequently use high-level planners to extract basic paths for agents, and then rely on local collision avoidance to ensure that the agents reach their destinations without colliding with one another or dynamic obstacles. One state-of-the-art local collision avoi...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.641.3408 2023-05-15T17:53:00+02:00 Reciprocally-Rotating Velocity Obstacles Andrew Giese Daniel Latypov Nancy M. Amato The Pennsylvania State University CiteSeerX Archives http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.3408 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.3408 Metadata may be used without restrictions as long as the oai identifier remains attached to it. https://parasol.tamu.edu/publications/download.php?file_id=869 text ftciteseerx 2016-01-08T15:58:36Z Abstract — Modern multi-agent systems frequently use high-level planners to extract basic paths for agents, and then rely on local collision avoidance to ensure that the agents reach their destinations without colliding with one another or dynamic obstacles. One state-of-the-art local collision avoidance technique is Optimal Reciprocal Collision Avoidance (ORCA). Despite being fast and efficient for circular-shaped agents, ORCA may deadlock when polygonal shapes are used. To address this shortcoming, we introduce Reciprocally-Rotating Velocity Obstacles (RRVO). RRVO generalizes ORCA by in-troducing a notion of rotation for polygonally-shaped agents. This generalization permits more realistic motion than ORCA and does not suffer from as much deadlock. In this paper, we present the theory of RRVO and show empirically that it does not suffer from the deadlock issue ORCA has, permits agents to reach goals faster, and has a comparable collision rate at the cost of performance overhead quadratic in the (typically small) user-defined parameter δ. I. Text Orca Unknown |
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Abstract — Modern multi-agent systems frequently use high-level planners to extract basic paths for agents, and then rely on local collision avoidance to ensure that the agents reach their destinations without colliding with one another or dynamic obstacles. One state-of-the-art local collision avoidance technique is Optimal Reciprocal Collision Avoidance (ORCA). Despite being fast and efficient for circular-shaped agents, ORCA may deadlock when polygonal shapes are used. To address this shortcoming, we introduce Reciprocally-Rotating Velocity Obstacles (RRVO). RRVO generalizes ORCA by in-troducing a notion of rotation for polygonally-shaped agents. This generalization permits more realistic motion than ORCA and does not suffer from as much deadlock. In this paper, we present the theory of RRVO and show empirically that it does not suffer from the deadlock issue ORCA has, permits agents to reach goals faster, and has a comparable collision rate at the cost of performance overhead quadratic in the (typically small) user-defined parameter δ. I. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Andrew Giese Daniel Latypov Nancy M. Amato |
spellingShingle |
Andrew Giese Daniel Latypov Nancy M. Amato Reciprocally-Rotating Velocity Obstacles |
author_facet |
Andrew Giese Daniel Latypov Nancy M. Amato |
author_sort |
Andrew Giese |
title |
Reciprocally-Rotating Velocity Obstacles |
title_short |
Reciprocally-Rotating Velocity Obstacles |
title_full |
Reciprocally-Rotating Velocity Obstacles |
title_fullStr |
Reciprocally-Rotating Velocity Obstacles |
title_full_unstemmed |
Reciprocally-Rotating Velocity Obstacles |
title_sort |
reciprocally-rotating velocity obstacles |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.3408 |
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Orca |
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Orca |
op_source |
https://parasol.tamu.edu/publications/download.php?file_id=869 |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.641.3408 |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766160754077073408 |