Bfm: A Scalable And Resource-Aware Method For Adaptive Mission Planning Of Uavs

UAVs must continuously adapt their mission to face unexpected internal or external hazards. This paper proposes a new BFM model (Bayesian Networks built from FMEA tables for MDP). This scalable model offers a modular and comprehensive method to incorporate different types of diagnosis modules based on BN (Bayesian Networks) and FMEA table (Failure Mode and Effects Analysis) to mission specifications expressed as a MDP (Markov Decision Processes). The BFM model implements the complete decision making process that covers both the application configurations at the embedded system level and the mission planning at the UAV level. These decisions are based on the QoS (Quality of Service) of applications, the resource use and the system and sensors health. We demonstrate on a case study for a target tracking mission that the BFM model can interface hazards and applications specifications and can improve the success and quality of the mission. To the best of our knowledge, this is the first proposal of a systematic method that integrates diagnosis modules to MDP model in order to take care of the implementation of embedded applications during a mission.