Robot Location Estimation in the Situation Calculus
This work addresses robot sensing in uncertain environments but appears incremental as it builds on existing frameworks without introducing new methods or broad improvements.
The paper tackles robot location estimation under uncertainty and noisy sensors by applying a general belief reasoning framework in the situation calculus to an example, showing that suitable posterior beliefs are entailed despite nonstandard action effects.
Location estimation is a fundamental sensing task in robotic applications, where the world is uncertain, and sensors and effectors are noisy. Most systems make various assumptions about the dependencies between state variables, and especially about how these dependencies change as a result of actions. Building on a general framework by Bacchus, Halpern and Levesque for reasoning about degrees of belief in the situation calculus, and a recent extension to it for continuous domains, in this paper we illustrate location estimation in the presence of a rich theory of actions using an example. We also show that while actions might affect prior distributions in nonstandard ways, suitable posterior beliefs are nonetheless entailed as a side-effect of the overall specification.