TY - CHAP
T1 - Intent-driven strategic tactical planning for autonomous site inspection using cooperative drones
AU - Buksz, Dorian
AU - Mujumdar, Anusha
AU - Orlic, Marin
AU - Mohalik, Swarup
AU - Daoutis, Marios
AU - Badrinath, Ramamurthy
AU - Magazzeni, Daniele
AU - Cashmore, Michael
AU - Vulgarakis Feljan, Aneta
N1 - Funding Information:
This work has been supported by the SCOTT project (Secure COnnected Trustable Things) (www.scottproject.eu), which has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737422.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - Realization of industry-scale, goal-driven, autonomous systems with AI planning technology faces several challenges: flexibly specifying planning goal states in varying situations, synthesizing plans in large state spaces, re-planning in dynamic situations, and facilitating humans to supervise, give feedback and intervene. In this paper, we present Intent-driven Strategic Tactical Planning (ISTP) to address these challenges. We demonstrate its efficacy through its application for radio base station inspection across several locations using drones. The inspection task involves capturing images, thermal images or signal measurements - called knowledge-objects - of various components of the base stations for downstream processing. In the ISTP approach, an operator indicates her goals by flying the drone to different components of interest. These goals are generalized to capture the intent of the operator, which are then instantiated in new situations to generate goals dynamically. Towards planning and re-planning in large state spaces to achieve these goals efficiently, we extend the Strategic-Tactical Planning paradigm. All the components of ISTP are integrated in an intuitive UI and demonstrated through a real life use-case built on the UNITY simulator platform.
AB - Realization of industry-scale, goal-driven, autonomous systems with AI planning technology faces several challenges: flexibly specifying planning goal states in varying situations, synthesizing plans in large state spaces, re-planning in dynamic situations, and facilitating humans to supervise, give feedback and intervene. In this paper, we present Intent-driven Strategic Tactical Planning (ISTP) to address these challenges. We demonstrate its efficacy through its application for radio base station inspection across several locations using drones. The inspection task involves capturing images, thermal images or signal measurements - called knowledge-objects - of various components of the base stations for downstream processing. In the ISTP approach, an operator indicates her goals by flying the drone to different components of interest. These goals are generalized to capture the intent of the operator, which are then instantiated in new situations to generate goals dynamically. Towards planning and re-planning in large state spaces to achieve these goals efficiently, we extend the Strategic-Tactical Planning paradigm. All the components of ISTP are integrated in an intuitive UI and demonstrated through a real life use-case built on the UNITY simulator platform.
UR - http://www.scopus.com/inward/record.url?scp=85102410076&partnerID=8YFLogxK
U2 - 10.1109/IROS45743.2020.9341440
DO - 10.1109/IROS45743.2020.9341440
M3 - Conference paper
AN - SCOPUS:85102410076
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6733
EP - 6740
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -