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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2309.08742 |
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| _version_ | 1866910906779172864 |
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| author | John, Yohan Hughes, Connor Diaz-Garcia, Gilberto Marden, Jason R. Bullo, Francesco |
| author_facet | John, Yohan Hughes, Connor Diaz-Garcia, Gilberto Marden, Jason R. Bullo, Francesco |
| contents | To enable the computation of effective randomized patrol routes for single- or multi-robot teams, we present RoSSO, a Python package designed for solving Markov chain optimization problems. We exploit machine-learning techniques such as reverse-mode automatic differentiation and constraint parametrization to achieve superior efficiency compared to general-purpose nonlinear programming solvers. Additionally, we supplement a game-theoretic stochastic surveillance formulation in the literature with a novel greedy algorithm and multi-robot extension. We close with numerical results for a police district in downtown San Francisco that demonstrate RoSSO's capabilities on our new formulations and the prior work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08742 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX John, Yohan Hughes, Connor Diaz-Garcia, Gilberto Marden, Jason R. Bullo, Francesco Robotics Optimization and Control To enable the computation of effective randomized patrol routes for single- or multi-robot teams, we present RoSSO, a Python package designed for solving Markov chain optimization problems. We exploit machine-learning techniques such as reverse-mode automatic differentiation and constraint parametrization to achieve superior efficiency compared to general-purpose nonlinear programming solvers. Additionally, we supplement a game-theoretic stochastic surveillance formulation in the literature with a novel greedy algorithm and multi-robot extension. We close with numerical results for a police district in downtown San Francisco that demonstrate RoSSO's capabilities on our new formulations and the prior work. |
| title | RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX |
| topic | Robotics Optimization and Control |
| url | https://arxiv.org/abs/2309.08742 |