1 research outputs found
Optimality and robustness in path-planning under initial uncertainty
Classical deterministic optimal control problems assume full information
about the controlled process. The theory of control for general
partially-observable processes is powerful, but the methods are computationally
expensive and typically address the problems with stochastic dynamics and
continuous (directly unobserved) stochastic perturbations. In this paper we
focus on path planning problems which are in between -- deterministic, but with
an initial uncertainty on either the target or the running cost on parts of the
domain. That uncertainty is later removed at some time , and the goal is to
choose the optimal trajectory until then. We address this challenge for three
different models of information acquisition: with fixed , discretely
distributed and exponentially distributed random . We develop models and
numerical methods suitable for multiple notions of optimality: based on the
average-case performance, the worst-case performance, the average constrained
by the worst, the average performance with probabilistic constraints on the bad
outcomes, risk-sensitivity, and distributional-robustness. We illustrate our
approach using examples of pursuing random targets identified at a (possibly
random) later time .Comment: 24 pages, 14 figures. Keywords: optimal control, path-planning,
Hamilton-Jacobi PDEs, uncertainty, robustness, delayed information
acquisitio