Combining Temporal Planning with Probabilistic Reasoning for Autonomous Surveillance Missions. / Bernardini, Sara; Fox, Maria; Long, Derek.

In: Autonomous Robots, Vol. 41, 01.2017, p. 181–203.

Research output: Contribution to journalArticlepeer-review

Published

Standard

Combining Temporal Planning with Probabilistic Reasoning for Autonomous Surveillance Missions. / Bernardini, Sara; Fox, Maria; Long, Derek.

In: Autonomous Robots, Vol. 41, 01.2017, p. 181–203.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bernardini, Sara ; Fox, Maria ; Long, Derek. / Combining Temporal Planning with Probabilistic Reasoning for Autonomous Surveillance Missions. In: Autonomous Robots. 2017 ; Vol. 41. pp. 181–203.

BibTeX

@article{e67adc68b6bc4f2d8d5d082b10e0aad0,
title = "Combining Temporal Planning with Probabilistic Reasoning for Autonomous Surveillance Missions",
abstract = "It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the “Parrot AR.Drone2.0” quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions.",
author = "Sara Bernardini and Maria Fox and Derek Long",
year = "2017",
month = jan,
doi = "10.1007/s10514-015-9534-0",
language = "English",
volume = "41",
pages = "181–203",
journal = "Autonomous Robots",
issn = "1573-7527",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Combining Temporal Planning with Probabilistic Reasoning for Autonomous Surveillance Missions

AU - Bernardini, Sara

AU - Fox, Maria

AU - Long, Derek

PY - 2017/1

Y1 - 2017/1

N2 - It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the “Parrot AR.Drone2.0” quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions.

AB - It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the “Parrot AR.Drone2.0” quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions.

U2 - 10.1007/s10514-015-9534-0

DO - 10.1007/s10514-015-9534-0

M3 - Article

VL - 41

SP - 181

EP - 203

JO - Autonomous Robots

JF - Autonomous Robots

SN - 1573-7527

ER -