Health Aware Stochastic Planning For Persistent Package Delivery Missions Using Quadrotors
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2014.Ali.Ure.ea.IROS_package.pdf
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Author(s)
Agha-mohammadi, Ali-akbar
Ure, Nazim Kemal
How, Jonathan P.
Vian, John
Date Issued
September 2014
Journal
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/IROS.2014.6943034
