Reducing Uncertainty in Navigation and Exploration

TL;DR

This paper presents a control system for mobile robots that manages environmental uncertainty by selecting actions based on expected information gain, integrating probabilistic decision models to improve navigation and exploration in unknown environments.

Contribution

It introduces a probabilistic decision-based control system that optimizes robot activities to reduce environmental uncertainty during navigation and exploration.

Findings

Effective reduction of environmental uncertainty in robot navigation

Integration of probabilistic models improves decision-making

Enhanced exploration efficiency in unknown environments

Abstract

A significant problem in designing mobile robot control systems involves coping with the uncertainty that arises in moving about in an unknown or partially unknown environment and relying on noisy or ambiguous sensor data to acquire knowledge about that environment. We describe a control system that chooses what activity to engage in next on the basis of expectations about how the information re- turned as a result of a given activity will improve 2 its knowledge about the spatial layout of its environment. Certain of the higher-level components of the control system are specified in terms of probabilistic decision models whose output is used to mediate the behavior of lower-level control components responsible for movement and sensing.