When to Localize? A POMDP Approach

TL;DR

This paper introduces a POMDP-based method for robots to optimally decide when to localize, balancing localization costs and failure risks in environments with hazards, improving navigation efficiency and safety.

Contribution

It formulates the problem as a Constrained POMDP and applies the Cost-Constrained POMCP solver to determine optimal localization timing, a novel approach for resource-aware robot navigation.

Findings

The method effectively reduces unnecessary localization actions.

It maintains failure probabilities within acceptable limits.

Numerical experiments show improved navigation performance.

Abstract

Robots often localize to lower navigational errors and facilitate downstream, high-level tasks. However, a robot may want to selectively localize when localization is costly (such as with resource-constrained robots) or inefficient (for example, submersibles that need to surface), especially when navigating in environments with variable numbers of hazards such as obstacles and shipping lanes. In this study, we propose a method that helps a robot determine ``when to localize'' to 1) minimize such actions and 2) not exceed the probability of failure (such as surfacing within high-traffic shipping lanes). We formulate our method as a Constrained Partially Observable Markov Decision Process and use the Cost-Constrained POMCP solver to plan the robot's actions. The solver simulates failure probabilities to decide if a robot moves to its goal or localizes to prevent failure. We performed numerical experiments with multiple baselines.