Health aware stochastic planning for persistent package delivery missions using quadrotors

In persistent missions, taking system's health and capability degradation into account is an essential factor to predict and avoid failures. The state space in health-aware planning problems is often a mixture of continuous vehicle-level and discrete mission-level states. This in particular poses a challenge when the mission domain is partially observable and restricts the use of computationally expensive forward search methods. This paper presents a method that exploits a structure that exists in many health-aware planning problems and performs a two-layer planning scheme. The lower layer exploits the local linearization and Gaussian distribution assumption over vehicle-level states while the higher layer maintains a non-Gaussian distribution over discrete mission-level variables. This two-layer planning scheme allows us to limit the expensive online forward search to the mission-level states, and thus predict system's behavior over longer horizons in the future. We demonstrate the performance of the method on a long duration package delivery mission using a quadrotor in a partially-observable domain in the presence of constraints and health/capability degradation.