High-speed autonomous navigation of unknown environments using learned probabilities of collision
We present a motion planning algorithm for dynamic vehicles navigating through unknown environments. We focus on the scenario in which a fast-moving car attempts to navigate from a start location to a set of goal coordinates in minimum time with no prior information about the environment, building a...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2018
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| Online Access: | http://hdl.handle.net/1721.1/116008 https://orcid.org/0000-0003-3765-2021 https://orcid.org/0000-0002-5867-4900 https://orcid.org/0000-0002-8293-0492 |
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| author | Richter, Charles Andrew Ware, John W. Roy, Nicholas |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Richter, Charles Andrew Ware, John W. Roy, Nicholas |
| author_sort | Richter, Charles Andrew |
| collection | MIT |
| description | We present a motion planning algorithm for dynamic vehicles navigating through unknown environments. We focus on the scenario in which a fast-moving car attempts to navigate from a start location to a set of goal coordinates in minimum time with no prior information about the environment, building a map in real time from onboard sensor data. Whereas existing planners for exploration confine themselves to a conservative set of constraints to guarantee safety around unknown regions of the environment, we instead learn a hazard function from data, which maps the vehicle's dynamic state and current environment knowledge to a probability of collision. We perform receding horizon planning in which the objective function is evaluated in expectation over those learned probabilities of collision. Our algorithm demonstrates sensible emergent behaviors, like swinging wide around blind corners, slowing down near the map frontier, and accelerating in regions of high visibility. Our algorithm is capable of navigating from start to goal much more quickly than the conservative baseline planner without sacrificing safety. We demonstrate our algorithm on a 1:8-scale high-performance RC car equipped with a planar laser range-finder and inertial measurement unit, reaching speeds of 4m/s in unknown, indoor spaces. A video of experimental results is available at: http: //groups.csail.mit.edu/rrg/nav-learned-prob-collision. |
| first_indexed | 2024-09-23T10:50:01Z |
| format | Article |
| id | mit-1721.1/116008 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:50:01Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1160082022-09-27T15:20:15Z High-speed autonomous navigation of unknown environments using learned probabilities of collision Richter, Charles Andrew Ware, John W. Roy, Nicholas Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Mechanical Engineering Richter, Charles Andrew Ware, John W. Roy, Nicholas We present a motion planning algorithm for dynamic vehicles navigating through unknown environments. We focus on the scenario in which a fast-moving car attempts to navigate from a start location to a set of goal coordinates in minimum time with no prior information about the environment, building a map in real time from onboard sensor data. Whereas existing planners for exploration confine themselves to a conservative set of constraints to guarantee safety around unknown regions of the environment, we instead learn a hazard function from data, which maps the vehicle's dynamic state and current environment knowledge to a probability of collision. We perform receding horizon planning in which the objective function is evaluated in expectation over those learned probabilities of collision. Our algorithm demonstrates sensible emergent behaviors, like swinging wide around blind corners, slowing down near the map frontier, and accelerating in regions of high visibility. Our algorithm is capable of navigating from start to goal much more quickly than the conservative baseline planner without sacrificing safety. We demonstrate our algorithm on a 1:8-scale high-performance RC car equipped with a planar laser range-finder and inertial measurement unit, reaching speeds of 4m/s in unknown, indoor spaces. A video of experimental results is available at: http: //groups.csail.mit.edu/rrg/nav-learned-prob-collision. 2018-05-31T12:51:27Z 2018-05-31T12:51:27Z 2014-09 2018-04-10T14:36:30Z Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-3685-4 http://hdl.handle.net/1721.1/116008 Richter, Charles, John Ware, and Nicholas Roy. “High-Speed Autonomous Navigation of Unknown Environments Using Learned Probabilities of Collision.” 2014 IEEE International Conference on Robotics and Automation (ICRA) (May 2014). https://orcid.org/0000-0003-3765-2021 https://orcid.org/0000-0002-5867-4900 https://orcid.org/0000-0002-8293-0492 http://dx.doi.org/10.1109/ICRA.2014.6907760 2014 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT Web Domain |
| spellingShingle | Richter, Charles Andrew Ware, John W. Roy, Nicholas High-speed autonomous navigation of unknown environments using learned probabilities of collision |
| title | High-speed autonomous navigation of unknown environments using learned probabilities of collision |
| title_full | High-speed autonomous navigation of unknown environments using learned probabilities of collision |
| title_fullStr | High-speed autonomous navigation of unknown environments using learned probabilities of collision |
| title_full_unstemmed | High-speed autonomous navigation of unknown environments using learned probabilities of collision |
| title_short | High-speed autonomous navigation of unknown environments using learned probabilities of collision |
| title_sort | high speed autonomous navigation of unknown environments using learned probabilities of collision |
| url | http://hdl.handle.net/1721.1/116008 https://orcid.org/0000-0003-3765-2021 https://orcid.org/0000-0002-5867-4900 https://orcid.org/0000-0002-8293-0492 |
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