Real-Time Motion Planning With Applications to Autonomous Urban Driving
This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plan...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/52527 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-0505-1400 https://orcid.org/0000-0002-2225-7275 |
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| author | Kuwata, Yoshiaki Teo, Justing Fiore, Gaston A. Karaman, Sertac Frazzoli, Emilio How, Jonathan P. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Kuwata, Yoshiaki Teo, Justing Fiore, Gaston A. Karaman, Sertac Frazzoli, Emilio How, Jonathan P. |
| author_sort | Kuwata, Yoshiaki |
| collection | MIT |
| description | This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2) safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles. |
| first_indexed | 2024-09-23T15:00:58Z |
| format | Article |
| id | mit-1721.1/52527 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:00:58Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/525272022-10-01T23:58:55Z Real-Time Motion Planning With Applications to Autonomous Urban Driving Kuwata, Yoshiaki Teo, Justing Fiore, Gaston A. Karaman, Sertac Frazzoli, Emilio How, Jonathan P. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Frazzoli, Emilio Kuwata, Yoshiaki Teo, Justin Fiore, Gaston A. Karaman, Sertac Frazzoli, Emilio How, Jonathan P. urban driving real-time motion planning rapidly-exploring random tree (RRT) dynamic and uncertain environment DARPA urban challenge autonomous This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2) safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles. Defense Advanced Research Projects Agency (Program: Urban Challenge, DARPA Order No. W369/00, Program Code: DIRO) 2010-03-11T21:18:42Z 2010-03-11T21:18:42Z 2009-08 2008-12 Article http://purl.org/eprint/type/JournalArticle 1063-6536 INSPEC Accession Number: 10841689 http://hdl.handle.net/1721.1/52527 Kuwata, Y. et al. “Real-Time Motion Planning With Applications to Autonomous Urban Driving.” Control Systems Technology, IEEE Transactions on 17.5 (2009): 1105-1118. © 2009 Institute of Electrical and Electronics Engineers https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-0505-1400 https://orcid.org/0000-0002-2225-7275 en_US http://dx.doi.org/10.1109/tcst.2008.2012116 IEEE Transactions on Control Systems Technology Article is made available in accordance with the publisher’s policy and may be subject to US copyright law. Please refer to the publisher’s site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | urban driving real-time motion planning rapidly-exploring random tree (RRT) dynamic and uncertain environment DARPA urban challenge autonomous Kuwata, Yoshiaki Teo, Justing Fiore, Gaston A. Karaman, Sertac Frazzoli, Emilio How, Jonathan P. Real-Time Motion Planning With Applications to Autonomous Urban Driving |
| title | Real-Time Motion Planning With Applications to Autonomous Urban Driving |
| title_full | Real-Time Motion Planning With Applications to Autonomous Urban Driving |
| title_fullStr | Real-Time Motion Planning With Applications to Autonomous Urban Driving |
| title_full_unstemmed | Real-Time Motion Planning With Applications to Autonomous Urban Driving |
| title_short | Real-Time Motion Planning With Applications to Autonomous Urban Driving |
| title_sort | real time motion planning with applications to autonomous urban driving |
| topic | urban driving real-time motion planning rapidly-exploring random tree (RRT) dynamic and uncertain environment DARPA urban challenge autonomous |
| url | http://hdl.handle.net/1721.1/52527 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0002-0505-1400 https://orcid.org/0000-0002-2225-7275 |
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