Contingency Constrained Planning with MPPI within MPPI
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866917867638751232 |
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| author | Jung, Leonard Estornell, Alexander Everett, Michael |
| author_facet | Jung, Leonard Estornell, Alexander Everett, Michael |
| contents | For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this research gap, this paper presents Contingency-MPPI, a data-driven optimization-based strategy that embeds contingency planning inside a nominal planner. By learning to approximate the optimal contingency-constrained control sequence with adaptive importance sampling, the proposed method's sampling efficiency is further improved with initializations from a lightweight path planner and trajectory optimizer. Finally, we present simulated and hardware experiments demonstrating our algorithm generating nominal and contingency plans in real time on a mobile robot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09777 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contingency Constrained Planning with MPPI within MPPI Jung, Leonard Estornell, Alexander Everett, Michael Robotics For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance between nominal and contingency behavior, or plan for a singular contingency plan; however, this does not guarantee that the resulting plan is safe for all time. To address this research gap, this paper presents Contingency-MPPI, a data-driven optimization-based strategy that embeds contingency planning inside a nominal planner. By learning to approximate the optimal contingency-constrained control sequence with adaptive importance sampling, the proposed method's sampling efficiency is further improved with initializations from a lightweight path planner and trajectory optimizer. Finally, we present simulated and hardware experiments demonstrating our algorithm generating nominal and contingency plans in real time on a mobile robot. |
| title | Contingency Constrained Planning with MPPI within MPPI |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.09777 |