TY - GEN
T1 - Reciprocally-Rotating Velocity Obstacles
AU - Giese, Andrew
AU - Latypov, Daniel
AU - Amato, Nancy M.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by Chevron, IBM, Intel, Orale/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2014/5
Y1 - 2014/5
N2 - © 2014 IEEE. Modern multi-agent systems frequently use highlevel 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 introducing 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.
AB - © 2014 IEEE. Modern multi-agent systems frequently use highlevel 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 introducing 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.
UR - http://hdl.handle.net/10754/599462
UR - http://ieeexplore.ieee.org/document/6907324/
UR - http://www.scopus.com/inward/record.url?scp=84929208695&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907324
DO - 10.1109/ICRA.2014.6907324
M3 - Conference contribution
SN - 9781479936854
SP - 3234
EP - 3241
BT - 2014 IEEE International Conference on Robotics and Automation (ICRA)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -