RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX
Yohan John, Connor Hughes, Gilberto Díaz-García, Jason R. Marden, Francesco Bullo
Abstract
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.
BibTeX
@inproceedings{icra2024_rossoahighperfor,
title = {RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX},
author = {Yohan John and Connor Hughes and Gilberto Díaz-García and Jason R. Marden and Francesco Bullo},
booktitle = {ICRA 2024},
year = {2024}
}