Collision Avoidance using Iterative Dynamic and Nonlinear Programming with Adaptive Grid Refinements
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916350080843776 |
|---|---|
| author | Richter, Rebecca De Marchi, Alberto Gerdts, Matthias |
| author_facet | Richter, Rebecca De Marchi, Alberto Gerdts, Matthias |
| contents | Nonlinear optimal control problems for trajectory planning with obstacle avoidance present several challenges. While general-purpose optimizers and dynamic programming methods struggle when adopted separately, their combination enabled by a penalty approach is capable of handling highly nonlinear systems while overcoming the curse of dimensionality. Nevertheless, using dynamic programming with a fixed state space discretization limits the set of reachable solutions, hindering convergence or requiring enormous memory resources for uniformly spaced grids. In this work we solve this issue by incorporating an adaptive refinement of the state space grid, splitting cells where needed to better capture the problem structure while requiring less discretization points overall. Numerical results on a space manipulator demonstrate the improved robustness and efficiency of the combined method with respect to the single components. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03148 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Collision Avoidance using Iterative Dynamic and Nonlinear Programming with Adaptive Grid Refinements Richter, Rebecca De Marchi, Alberto Gerdts, Matthias Optimization and Control Robotics Nonlinear optimal control problems for trajectory planning with obstacle avoidance present several challenges. While general-purpose optimizers and dynamic programming methods struggle when adopted separately, their combination enabled by a penalty approach is capable of handling highly nonlinear systems while overcoming the curse of dimensionality. Nevertheless, using dynamic programming with a fixed state space discretization limits the set of reachable solutions, hindering convergence or requiring enormous memory resources for uniformly spaced grids. In this work we solve this issue by incorporating an adaptive refinement of the state space grid, splitting cells where needed to better capture the problem structure while requiring less discretization points overall. Numerical results on a space manipulator demonstrate the improved robustness and efficiency of the combined method with respect to the single components. |
| title | Collision Avoidance using Iterative Dynamic and Nonlinear Programming with Adaptive Grid Refinements |
| topic | Optimization and Control Robotics |
| url | https://arxiv.org/abs/2311.03148 |