This paper describes a Python toolbox for active perception and control synthesis of probabilistic signal temporal logic (PrSTL) formulas of switched linear systems with additive Gaussian disturbances and measurement noises. We implement a counterexample-guided synthesis strategy that combines Bounded Model Checking, linear programming, and sampling-based motion planning techniques. We illustrate our approach and the toolbox throughout the paper with a motion planning example for a vehicle with noisy localization. The code is available at \url{https://codeocean.com/capsule/0013534/tree}.
@article{arxiv.2111.02943,
title = {idSTLPy: A Python Toolbox for Active Perception and Control},
author = {Rafael Rodrigues da Silva and Kunal Yadav and Hai Lin},
journal= {arXiv preprint arXiv:2111.02943},
year = {2021}
}
Comments
6 pages. arXiv admin note: text overlap with arXiv:2111.02226