BFM: a scalable and resourceaware 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 an MDP (Markov Decision Processes). The BFM model implements the complete decisionmaking 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.

Fast Runtime Monitoring, Replanning, and Recovery for Safe Autonomous System Operations In this paper, we present a fast runtime monitoring framework for safety assurance during autonomous system operations in uncertain environments. Modern unmanned vehicles rely on periodic sensor measurements for motion planning and control. However, a vehicle may not always be able to obtain its state information due to various reasons such as sensor failures, signal occlusions, and communication problems. To guarantee the safety of a system during these circumstances under the presence of disturbance and noise, we propose a novel fast reachability analysis approach that leverages Gaussian process regression theory to predict future states of the system at runtime. We also propose a selfeventtriggered monitoring and replanning approach which leverages our fast reachability scheme to recover the system when needed and replan its trajectory to guarantee safety constraints (i.e., the system will not collide with any obstacles). Our technique is validated both with simulations and experiments on unmanned aerial vehicles case studies in cluttered environments under the effect of unknown wind disturbance at runtime.