Robot Planning in Uncertain, Dynamic Environments
Many real-world applications require robots to operate in dynamic environments characterized by moving objects or agents whose trajectories are unpredictable. This thesis addresses the challenges posed by such environments by introducing Relative Temporal Probabilistic Roadmaps (Rel-T-PRM), a novel...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156644 |
| _version_ | 1826212737370292224 |
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| author | Cheerla, Anika |
| author2 | Lozano-Perez, Tomás |
| author_facet | Lozano-Perez, Tomás Cheerla, Anika |
| author_sort | Cheerla, Anika |
| collection | MIT |
| description | Many real-world applications require robots to operate in dynamic environments characterized by moving objects or agents whose trajectories are unpredictable. This thesis addresses the challenges posed by such environments by introducing Relative Temporal Probabilistic Roadmaps (Rel-T-PRM), a novel motion planning algorithm that builds upon the Temporal Probabilistic Roadmap (T-PRM) algorithm. The Rel-T-PRM allows for variable dynamic obstacle size, enables robustness with respect to minor changes in time and position and and introduces the concept of waiting until obstacles clear. Furthermore, we leverage Rel-T-PRM’s strengths to propose two replanning strategies. The first attempts to rapidly replan on-the-fly by using waiting to modify the trajectory without needing to modify the path. The second proposed replanning strategy identifies and plans to safe locations, where the robot can safely replan under a longer time horizon. We demonstrate Rel-T-PRM through a variety of simulation experiments on a fixed-base robotic manipulator. |
| first_indexed | 2024-09-23T15:36:48Z |
| format | Thesis |
| id | mit-1721.1/156644 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:36:48Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1566442024-09-04T03:08:45Z Robot Planning in Uncertain, Dynamic Environments Cheerla, Anika Lozano-Perez, Tomás Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Many real-world applications require robots to operate in dynamic environments characterized by moving objects or agents whose trajectories are unpredictable. This thesis addresses the challenges posed by such environments by introducing Relative Temporal Probabilistic Roadmaps (Rel-T-PRM), a novel motion planning algorithm that builds upon the Temporal Probabilistic Roadmap (T-PRM) algorithm. The Rel-T-PRM allows for variable dynamic obstacle size, enables robustness with respect to minor changes in time and position and and introduces the concept of waiting until obstacles clear. Furthermore, we leverage Rel-T-PRM’s strengths to propose two replanning strategies. The first attempts to rapidly replan on-the-fly by using waiting to modify the trajectory without needing to modify the path. The second proposed replanning strategy identifies and plans to safe locations, where the robot can safely replan under a longer time horizon. We demonstrate Rel-T-PRM through a variety of simulation experiments on a fixed-base robotic manipulator. M.Eng. 2024-09-03T21:14:16Z 2024-09-03T21:14:16Z 2024-05 2024-07-11T14:36:09.294Z Thesis https://hdl.handle.net/1721.1/156644 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Cheerla, Anika Robot Planning in Uncertain, Dynamic Environments |
| title | Robot Planning in Uncertain, Dynamic Environments |
| title_full | Robot Planning in Uncertain, Dynamic Environments |
| title_fullStr | Robot Planning in Uncertain, Dynamic Environments |
| title_full_unstemmed | Robot Planning in Uncertain, Dynamic Environments |
| title_short | Robot Planning in Uncertain, Dynamic Environments |
| title_sort | robot planning in uncertain dynamic environments |
| url | https://hdl.handle.net/1721.1/156644 |
| work_keys_str_mv | AT cheerlaanika robotplanninginuncertaindynamicenvironments |