Multi-Vehicle Health Management

Coordinated multi-vehicle autonomous systems have the potential to provide incredible functionality, but off-nominal conditions and degraded system components can render this capability ineffective. This research effort is focused on developing techniques to improve mission-level functional reliability through better system self-awareness and adaptive mission planning. This extends the traditional definition of health management, which has historically referred to the process of actively monitoring and managing vehicle sub-systems (e.g., avionics) in the event of component failures, to the context of multiple vehicle operations and autonomous multi-agent teams. In this case, health management information about each mission system component is used to improve the mission system's self-awareness and adapt vehicle, guidance, task and mission plans. As discussed in the following, the focus of our work has been on establishing the theoretical foundations of health-aware task planning, developing tractable solution algorithms to the resulting optimizations, and using a unique multi-UAV testbed called RAVEN to experimentally demonstrate the benefits of the new approach.