Health Aware Stochastic Planning For Persistent Package Delivery Missions Using Quadrotors
Author(s)Agha-mohammadi, Ali-akbar; Ure, Nazim Kemal; How, Jonathan P.; Vian, John
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
Institute of Electrical and Electronics Engineers (IEEE)
Agha-mohammadi, Ali-akbar, Nazim Kemal Ure, Jonathan P. How, and John Vian. "Health Aware Stochastic Planning For Persistent Package Delivery Missions Using Quadrotors." IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, September 14-18, 2014, pp.3389-3396.
Author's final manuscript
INSPEC Accession Number: 14718074