Robustness-driven Exploration with Probabilistic Metric Temporal Logic

Xiaotian Liu, Pengyi Shi, Tongtong Liu, Sarra Alqahtani, Paul Pauca, Miles Silman

2021

Abstract

The ability to perform autonomous exploration is essential for unmanned aerial vehicles (UAV) operating in unknown environments where it is difficult to describe the environment beforehand. Algorithms for autonomous exploration often focus on optimizing time and full coverage in a greedy fashion. These algorithms can collect irrelevant data and wastes time navigating areas with no important information. In this paper, we aim to improve the efficiency of exploration by maximizing the probability of detecting valuable information. The proposed approach relies on a theory of robustness based on Probabilistic Metric Temporal Logic (P-MTL) which is traditionally applied to offline verification and online control of hybrid systems. The robustness values would guide the UAV towards areas with more significant information by maximizing the satisfaction of the predefined P-MTL specifications. Markov Chain Monte Carlo (MCMC) is utilized to solve the P-MTL constraints. We tested our approach over Amazonian rainforest to detect areas occupied by illegal Artisanal Small-scale Gold Mining (ASGM) activities. The results show that our approach outperforms a greedy exploration approach from the literature by 38% in terms of ASGM coverage.

Paper Citation

in Harvard Style

Liu X., Shi P., Liu T., Alqahtani S., Pauca P. and Silman M. (2021). Robustness-driven Exploration with Probabilistic Metric Temporal Logic.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 57-66. DOI: 10.5220/0010192100570066

in Bibtex Style

@conference{icaart21,
author={Xiaotian Liu and Pengyi Shi and Tongtong Liu and Sarra Alqahtani and Paul Pauca and Miles Silman},
title={Robustness-driven Exploration with Probabilistic Metric Temporal Logic},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={57-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010192100570066},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Robustness-driven Exploration with Probabilistic Metric Temporal Logic
SN - 978-989-758-484-8
AU - Liu X.
AU - Shi P.
AU - Liu T.
AU - Alqahtani S.
AU - Pauca P.
AU - Silman M.
PY - 2021
SP - 57
EP - 66
DO - 10.5220/0010192100570066