The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
ClearPath: highly parallel collision avoidance for multi-agent simulation
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.
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Citations
Cites
Flocks, herds and schools: A distributed behavioral model
Reynolds, C. W. (1987) · Proceedings of the 14th annual conference on Computer graphics and interactive techniques · doi:10.1145/37401.37406 · Cited by 7,714
LaValle, S. M. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546877 · Cited by 5,211
Flocks, herds and schools: A distributed behavioral model
Reynolds, C. W. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37406 · Cited by 5,148
LaValle, S. M. (2006) · Cited by 4,177
Motion Planning in Dynamic Environments Using Velocity Obstacles
Fiorini, P., & Shiller, Z. (1998) · The International Journal of Robotics Research · doi:10.1177/027836499801700706 · Cited by 2,008
Reciprocal n-Body Collision Avoidance
van den Berg, J., Guy, S. J., Lin, M., & Manocha, D. (2011) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-642-19457-3_1 · Cited by 1,858
Self-citationReciprocal Velocity Obstacles for real-time multi-agent navigation
van den Berg, J., Lin, M., & Manocha, D. (2008) · 2008 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2008.4543489 · Cited by 1,722
Self-citationSelf-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005) · Transportation Science · doi:10.1287/trsc.1040.0108 · Cited by 1,471
Ericson, C. (2004) · CRC Press eBooks · doi:10.1201/b14581 · Cited by 1,196
Pedestrian and evacuation dynamics
Schreckenberg, M., & Sharma, S. D. (2002) · Springer eBooks · Cited by 1,018
Cited by
The Hybrid Reciprocal Velocity Obstacle
Snape, J., Berg, J. V. D., Guy, S. J., & Manocha, D. (2011) · IEEE Transactions on Robotics · doi:10.1109/tro.2011.2120810 · Cited by 448
Self-citationReciprocal collision avoidance with acceleration-velocity obstacles
van den Berg, J., Snape, J., Guy, S. J., & Manocha, D. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980408 · Cited by 288
Self-citationOptimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots
Alonso-Mora, J., Breitenmoser, A., Rufli, M., Beardsley, P., & Siegwart, R. (2013) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-642-32723-0_15 · Cited by 280
A Deep Reinforcement Learning Method for Mobile Robot Collision Avoidance based on Double DQN
Xue, X., Li, Z., Zhang, D., & Yan, Y. (2019) · 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2019.8781522 · Cited by 48
Metadata
- Authors
- Guy, S. J., Chhugani, J., Kim, C., Satish, N., Lin, M., Manocha, D., & Dubey, P.
- Venue
- Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
- Publisher
- ACM
- Published
- 2009-08
- Pages
- 177-187
- OpenAlex
- W1967907012
- Cited by
- 291
- References
- 29
- FWCI
- 15.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Guy, S. J., Chhugani, J., Kim, C., Satish, N., Lin, M., Manocha, D., & Dubey, P. (2009). ClearPath: highly parallel collision avoidance for multi-agent simulation. Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. https://doi.org/10.1145/1599470.1599494
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
