Anytime Sensing, Planning and Action: A Practical Model for Robot Control
Shlomo Zilberstein, Stuart Russell · 1993
Anytime algorithms, whose quality of results improves gradually as computation time increases, provide useful performance components for timecritical planning and control of robotic systems. In earlier work, we introduced a compilation scheme for optimal composition of anytime algorithms. In this paper we present an implementation of a navigation system in which an off-line compilation process and a run-time monitoring component guarantee the optimal allocation of time to the anytime modules. The crucial meta-level knowledge is kept in the anytime library in the form of conditional performance profiles. We also extend the notion of gradual improvement to sensing and plan execution.