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2011 Vol. 16, #4 - Robust Mission Planning
How do plans based on deterministic models fare in the face of uncertainty? More importantly, are there good methods for ‘‘robust-ing’’ plans to allow them to endure in a dynamic environment? In this paper, the authors extend robust optimization methods to a task assignment problem for unmanned aerial systems and analyze their performance in a simulated environment. The protection offered by robust plans comes with reasonable computational effort, but allows demonstrably superior solutions with respect to time before plan failure. There is also a trade-off between over-protecting against uncertainty and the goodness of the plan. This work is part of an autonomous planning framework being developed at Draper Lab and MIT.
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