Improving Trajectory Optimization Using a Roadmap Framework
© 2018 IEEE. We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 commo...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137335 |
| _version_ | 1811093725695180800 |
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| author | Dai, Siyu Orton, Matthew Ralph Schaffert, Shawn Hofmann, Andreas Williams, Brian C |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Dai, Siyu Orton, Matthew Ralph Schaffert, Shawn Hofmann, Andreas Williams, Brian C |
| author_sort | Dai, Siyu |
| collection | MIT |
| description | © 2018 IEEE. We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application scenarios with 5000 test cases each, we show that optimization-based or sampling-based planners alone are not effective for realistic problems where fast planning times are required. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the combination of our sparse roadmap and trajectory optimization provides superior performance over other standard sampling-based planners' combinations. By using a multi-query roadmap instead of generating completely new trajectories for each planning problem, our approach allows for extensions such as persistent control policy information associated with a trajectory across planning problems. Also, the sub-optimality resulting from the sparsity of roadmap, as well as the unexpected disturbances from the environment, can both be overcome by the real-time trajectory optimization process. |
| first_indexed | 2024-09-23T15:49:39Z |
| format | Article |
| id | mit-1721.1/137335 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:49:39Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1373352022-09-29T16:24:22Z Improving Trajectory Optimization Using a Roadmap Framework Dai, Siyu Orton, Matthew Ralph Schaffert, Shawn Hofmann, Andreas Williams, Brian C Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Aeronautics and Astronautics © 2018 IEEE. We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application scenarios with 5000 test cases each, we show that optimization-based or sampling-based planners alone are not effective for realistic problems where fast planning times are required. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the combination of our sparse roadmap and trajectory optimization provides superior performance over other standard sampling-based planners' combinations. By using a multi-query roadmap instead of generating completely new trajectories for each planning problem, our approach allows for extensions such as persistent control policy information associated with a trajectory across planning problems. Also, the sub-optimality resulting from the sparsity of roadmap, as well as the unexpected disturbances from the environment, can both be overcome by the real-time trajectory optimization process. 2021-11-04T14:51:25Z 2021-11-04T14:51:25Z 2018-10 2021-05-04T18:41:36Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137335 Dai, Siyu, Orton, Matthew Ralph, Schaffert, Shawn, Hofmann, Andreas and Williams, Brian C. 2018. "Improving Trajectory Optimization Using a Roadmap Framework." IEEE International Conference on Intelligent Robots and Systems. en http://dx.doi.org/10.1109/iros.2018.8594274 IEEE International Conference on Intelligent Robots and Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Dai, Siyu Orton, Matthew Ralph Schaffert, Shawn Hofmann, Andreas Williams, Brian C Improving Trajectory Optimization Using a Roadmap Framework |
| title | Improving Trajectory Optimization Using a Roadmap Framework |
| title_full | Improving Trajectory Optimization Using a Roadmap Framework |
| title_fullStr | Improving Trajectory Optimization Using a Roadmap Framework |
| title_full_unstemmed | Improving Trajectory Optimization Using a Roadmap Framework |
| title_short | Improving Trajectory Optimization Using a Roadmap Framework |
| title_sort | improving trajectory optimization using a roadmap framework |
| url | https://hdl.handle.net/1721.1/137335 |
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