The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Motion planning for urban driving using RRT
Abstract
This paper provides a detailed analysis of the motion planning subsystem for the MIT DARPA Urban Challenge vehicle. The approach is based on the Rapidly-exploring Random Trees (RRT) algorithm. The purpose of this paper is to present the numerous extensions made to the standard RRT algorithm that enable the on-line use of RRT on robotic vehicles with complex, unstable dynamics and significant drift, while preserving safety in the face of uncertainty and limited sensing. The paper includes numerous simulation and race results that clearly demonstrate the effectiveness of the planning system.
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Citations
Cites
A perception‐driven autonomous urban vehicle
Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20262 · Cited by 429
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Cited by
A Review of Motion Planning Techniques for Automated Vehicles
Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498841 · Cited by 1,670
Optimization‐based autonomous racing of 1:43 scale RC cars
Liniger, A., Domahidi, A., & Morari, M. (2015) · Optimal Control Applications and Methods · doi:10.1002/oca.2123 · Cited by 537
Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework
Rosolia, U., & Borrelli, F. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2753460 · Cited by 341
Habitat 2.0: Training home assistants to rearrange their habitat
Szot, A., Clegg, A., Undersander, E., Wijmans, E., Zhao, Y., Turner, J., Maestre, N., Mukadam, M., Chaplot, D., Maksymets, O., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.14405 · Cited by 143
Metadata
- Authors
- Kuwata, Y., Fiore, G. A., Teo, J., Frazzoli, E., & How, J. P.
- Venue
- 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 1681-1686
- OpenAlex
- W2103639171
- Cited by
- 217
- References
- 1
- FWCI
- 7.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Motion planning for urban driving using RRT. https://app.cochat.ai/papers/10.1109/iros.2008.4651075
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- Kuwata, Y., Fiore, G. A., Teo, J., Frazzoli, E., & How, J. P. (2008). Motion planning for urban driving using RRT. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2008.4651075
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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.
