In this paper, we describe an approach to planning sensing strategies dynamically, based on the system’s current best information about the world. Our approach is for the system to automatically propose a sensing operation, and then to determine the maximum ambiguity which might remain in the world description if that sensing operation were applied. The system then applies that sensing operation which minimizes this ambiguity. To do this, the system formulates object hypotheses and assesses its relative belief in those hypotheses to predict what features might be observed by a proposed sensing operation. Furthermore, since the number of sensing operations available to the system can be arbitrarily large, we group together equivalent sensing operations using a data structure that is based on the aspect graph. Finally, in order to measure the ambiguity in a set of hypotheses, we apply the concept of entropy from information theory. This allows us to determine the ambiguity in a hypothesis set in terms of the number of hypotheses and the system’s distribution of belief amongst those hypotheses.
|Original language||English (US)|
|Number of pages||19|
|Journal||IEEE Transactions on Robotics and Automation|
|State||Published - Dec 1989|
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering