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Sensors 2018, 18(12), 4266; https://doi.org/10.3390/s18124266

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

1
Lab-STICC, CNRS, Université de Bretagne Occidentale, 29200 Brest France, France
2
CNRS, Grenoble-INP, Inria, LIG, University of Grenoble-Alpes, 38000 Grenoble, France
3
Lab-STICC, CNRS, Université de Bretagne Sud, 56100 Lorient, France
*
Authors to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 16 November 2018 / Accepted: 27 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Reconfigurable Sensor Drones)
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Abstract

This paper presents a scalable approach to model uncertainties within a UAV (Unmanned Aerial Vehicle) embedded mission manager. It proposes a concurrent version of BFM models, which are Bayesian Networks built from FMEA (Failure Mode and Effects Analysis) and used by MDPs (Markov Decision Processes). The models can separately handle different applications during the mission; they consider the context of the mission including external constraints (luminosity, climate, etc.), the health of the UAV (Energy, Sensor) as well as the computing resource availability including CPU (Central Processing Unit) load, FPGA (Field Programmable Gate Array) use and timing performances. The proposed solution integrates the constraints into a mission specification by means of FMEA tables in order to facilitate their specifications by non-experts. Decision-making processes are elaborated following a “just enough” quality management by automatically providing adequate implementation of the embedded applications in order to achieve the mission goals, in the context given by the sensors and the on-board monitors. We illustrate the concurrent BFM approach with a case study of a typical tracking UAV mission. This case also considers a FPGA-SoC (FPGA-System on Chip) platform into consideration and demonstrates the benefits to tune the quality of the embedded applications according to the environmental context. View Full-Text
Keywords: fault recovery; anomaly detection; diagnosis; mission planning; Markov Decision Process; Bayesian Networks; System-on-Chip fault recovery; anomaly detection; diagnosis; mission planning; Markov Decision Process; Bayesian Networks; System-on-Chip
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Hireche, C.; Dezan, C.; Mocanu, S.; Heller, D.; Diguet, J.-P. Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs. Sensors 2018, 18, 4266.

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