Model-based System Health Management and Contingency Planning for Autonomous UAS

Safe autonomous operations of an Unmanned Aerial System (UAS) requires that the UAS can react to unforeseen circumstances, for example, after a failure has occurred. In this paper we describe a model-based run-time architecture for autonomous on-board diagnosis, system health management, and contingency management. This architecture is being instantiated on top of NASA's Core Flight System (cFS/cFE) as amajor component of the on-board AutonomousOperating System (AOS). We will describe our diagnosis and monitoring components, which continuously provide system health status. Automated reasoning with constraint satisfaction form the core of our decision-making component, which assesses the current situation, aids in failure disambiguation, and constructs a contingency plan to mitigate the failure(s) and allow for a safe end of the mission. We will illustrate our contingency management system with two case studies, one for a fixed-wing aircraft in simulation, and one for an autonomous DJI S1000+ octo-copter

An intelligent agent based autonomous mission management system for UAVs.

Unmanned Aerial Vehicles (UAVs) have been around for almost eight decades, but they evolved from the basic remotely-controlled form only during the last two. While UAV-related research encompasses several areas, the increase of autonomy is certainly one of the most important topics. Current-generation UAVs are typically able to autonomously execute a pre-planned mission, and there is a definite trend towards increased UAV autonomy. The main challenge related to UAV autonomy is the reaction to unforeseen events; the capability to execute pre-planned tasks does not take into account the fact that in a dynamic environment the plan might need to be changed. This Mission Management activity is usually tasked to human supervision. Within this thesis, the problem of UAV autonomy will be addressed by proposing a software architecture that allows the integration of various technologies to obtain a significant improvement in the autonomy level of UAV. The first step in this is the definition of a set of theoretical concepts that allow the computational description of three different types of information: user-generated mission objectives, knowledge regarding the external environment and complete flight plans. The second step is then to develop a software system that autonomously accomplishes the Mission Management task: combining mission objectives and environmental knowledge to generate a viable flight plan, then update it when situational awareness changes. The software system presented in this thesis is implemented using a combination of three separate Soar Intelligent Agents and traditional control techniques. Soar (State, Operator and Result) is the computational implementation of a general theory of cognition, allowing for the definition of complex software agents which can efficiently apply the rules defining their behaviour to large amounts of knowledge. Soar agents are used to provide high-level reasoning capability to the system, while low-level control functions are better performed by traditional algorithms. The software system is fully described and implemented, then tested in a simulation environment. Simulations are used to demonstrate the system’s ability to automatically generate, execute and update (when necessary) an entire flight plan after being assigned a set of mission objectives