RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX

TL;DR

RoSSO is a high-performance Python package that leverages JAX and machine learning techniques to optimize robotic surveillance routes efficiently, incorporating novel algorithms for multi-robot scenarios and demonstrating effectiveness through real-world case studies.

Contribution

The paper introduces RoSSO, a Python package that uses advanced optimization and machine learning methods for efficient surveillance route planning, including new algorithms for multi-robot coordination.

Findings

RoSSO outperforms traditional solvers in efficiency.

The package effectively handles multi-robot surveillance problems.

Numerical results validate the proposed formulations on real-world data.

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.