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A stochastic approach for attack resilient UAV motion planning

Title
A stochastic approach for attack resilient UAV motion planning
Authors
Bezzo, NicolaWeimer, JamesDu, YanweiSokolsky, OlegSon, Sang H.Lee, Insup
DGIST Authors
Son, Sang H.
Issue Date
2016
Citation
2016 American Control Conference, ACC 2016, 2016-July, 1366-1372
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
ISSN
0743-1619
Abstract
In this paper we propose a stochastic strategy for motion planning of unmanned aerial vehicles (UAVs) subject to malicious cyber attacks. By injecting erroneous information and compromising sensor data, an attacker can hijack a system driving it to unsafe states. In this work we bear on the problem of choosing optimal actions while one or more sensors are not reliable. We assume that the system is fully observable and one or more measurements (however unknown) return incorrect estimates of a state. We build an algorithm that leverages the theory of Markov decision processes (MDPs) to determine the optimal policy to plan the motion of a UAV and avoid unsafe regions of a state space. We name Redundant Observable MDPs (ROMDPs) this class of markovian processes that deal with redundant attacked measurements. A quadrotor case study is introduced and simulation and experimental results are presented to validate the proposed strategy. © 2016 American Automatic Control Council (AACC).
URI
http://hdl.handle.net/20.500.11750/3673
DOI
10.1109/ACC.2016.7525108
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Son, Sang Hyuk RTCPS(Real-Time Cyber-Physical Systems Research) Lab
  • Research Interests
Files:
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Collection:
ETC2. Conference Papers


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