ClearPath: highly parallel collision avoidance for multi-agent simulation Export

@inproceedings{citeulike:5743678, abstract = {We present a new local collision avoidance algorithm between multiple agents for real-time simulations. Our approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent. This resulting algorithm can be parallelized by exploiting data-parallelism and thread-level parallelism. The overall approach, ClearPath, is general and can robustly handle dense scenarios with tens or hundreds of thousands of heterogeneous agents in a few milli-seconds. As compared to prior collision avoidance algorithms, we observe more than an order of magnitude performance improvement.}, address = {New York, NY, USA}, author = {Stephen and Chhugani, Jatin and Kim, Changkyu and Satish, Nadathur and Lin, Ming and Manocha, Dinesh and Dubey, Pradeep}, booktitle = {SCA '09: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation}, citeulike-article-id = {5743678}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1599470.1599494}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1599470.1599494}, doi = {10.1145/1599470.1599494}, isbn = {978-1-60558-610-6}, keywords = {crowd, gpgpu, group, navigation, parallel, steering}, location = {New Orleans, Louisiana}, pages = {177--187}, posted-at = {2009-09-08 19:18:16}, priority = {2}, publisher = {ACM}, title = {ClearPath: highly parallel collision avoidance for multi-agent simulation}, url = {http://dx.doi.org/10.1145/1599470.1599494}, year = {2009} }

TY - CONF ID - citeulike:5743678 L3 - citeulike-article-id:5743678 N2 - We present a new local collision avoidance algorithm between multiple agents for real-time simulations. Our approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent. This resulting algorithm can be parallelized by exploiting data-parallelism and thread-level parallelism. The overall approach, ClearPath, is general and can robustly handle dense scenarios with tens or hundreds of thousands of heterogeneous agents in a few milli-seconds. As compared to prior collision avoidance algorithms, we observe more than an order of magnitude performance improvement. CY - New Orleans, Louisiana SN - 978-1-60558-610-6 AD - New York, NY, USA EP - 187 TI - ClearPath: highly parallel collision avoidance for multi-agent simulation PB - ACM T2 - SCA '09: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation SP - 177 KW - crowd KW - gpgpu KW - group KW - navigation KW - parallel KW - steering AU - Stephen AU - Chhugani, Jatin AU - Kim, Changkyu AU - Satish, Nadathur AU - Lin, Ming AU - Manocha, Dinesh AU - Dubey, Pradeep PY - 2009/// UR - http://dx.doi.org/10.1145/1599470.1599494 ER -